This thesis presents the development of computational methods and tools using as input three-dimensional structures data of protein-ligand complexes. The tools are useful to mine, profile and predict data from protein-ligand complexes to improve the modeling and the understanding of the protein-ligand recognition. This thesis is divided into five sub-projects. In addition, unpublished results about positioning water molecules in binding pockets are also presented. I developed a statistical model, PockDrug, which combines three properties (hydrophobicity, geometry and aromaticity) to predict the druggability of protein pockets, with results that are not dependent on the pocket estimation methods. The performance of pockets estimated on apo or holo proteins is better than that previously reported in the literature (Publication I). PockDrug is made available through a web server, PockDrug-Server (http://pockdrug.rpbs.univ-paris-diderot.fr), which additionally includes many tools for protein pocket analysis and characterization (Publication II). I developed a customizable computational workflow based on the superimposition of homologous proteins to mine the structural replacements of functional groups in the Protein Data Bank (PDB). Applied to phosphate groups, we identified a surprisingly high number of phosphate non-polar replacements as well as some mechanisms allowing positively charged replacements. In addition, we observed that ligands adopted a U-shape conformation at nucleotide binding pockets across phylogenetically unrelated proteins (Publication III). I investigated the prevalence of salt bridges at protein-ligand complexes in the PDB for five basic functional groups. The prevalence ranges from around 70% for guanidinium to 16% for tertiary ammonium cations, in this latter case appearing to be connected to a smaller volume available for interacting groups. In the absence of strong carboxylate-mediated salt bridges, the environment around the basic functional groups studied appeared enriched in functional groups with acidic properties such as hydroxyl, phenol groups or water molecules (Publication IV). I developed a tool that allows the analysis of binding poses obtained by docking. The tool compares a set of docked ligands to a reference bound ligand (may be different molecule) and provides a graphic output that plots the shape overlap and a Jaccard score based on comparison of molecular interaction fingerprints. The tool was applied to analyse the docking poses of active ligands at the orexin-1 and orexin-2 receptors found as a result of a combined virtual and experimental screen (Publication V). The review of literature focusses on protein-ligand recognition, presenting different concepts and current challenges in drug discovery.
Role of bioinformatics and pharmacogenomics in drug discoveryArindam Chakraborty
Bioinformatics and pharmacogenomics can accelerate drug discovery and development processes and reduce costs and timelines. Bioinformatics provides databases and tools to aid in target identification and validation. Pharmacogenomics helps determine individual genetic factors that influence drug responses. Together, they allow more efficient and personalized drug development. While still developing, bioinformatics and pharmacogenomics show potential to support drug design and address barriers like adverse reactions. They may help revive orphan drugs and aid in developing treatments for emerging issues like COVID-19 through drug repurposing informed by human genome interactions.
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...Robert Hoehndorf
This document discusses integrating knowledge from pharmacogenomics databases to enable personalized medicine approaches. It describes using ontologies to integrate data on drugs, diseases, pathways, and genotypes from multiple sources. Queries over this integrated knowledge can discover disease and drug pathways and relationships between genotypes and drugs/diseases. The goal is to identify aberrant pathways underlying a disease and personalized treatment options based on a patient's gene expression profile. Future work includes expanding to interaction networks and experimental validation.
Requesting a complete biosensor system in phyto-sourced drug discovery and de...iosrphr_editor
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
This document outlines the syllabus for the 7th level open and internal competitive examination for Medical Pharmacist conducted by the Public Service Commission of Nepal.
The examination will have two parts - a written exam worth 200 marks and an oral/group test worth 40 marks. The written exam will consist of two parts - a 100 mark multiple choice questions paper and a 100 mark subjective questions paper.
The syllabus covers key topics in pharmacy including development and legislation in Nepal, pharmaceutical analysis, pharmaceutics, pharmacognosy, pharmacology, medicinal chemistry, drug acts and pharmacopoeia, and pharmaceutical care and drug supply management. Sample multiple choice and subjective questions are also provided.
The oral/group test
Bioinformatics role in Pharmaceutical industriesMuzna Kashaf
Bioinformatics plays a key role in the pharmaceutical industry by enabling target identification of diseases, rational drug design, compound refinement, and other processes. It facilitates identifying target diseases and compounds, detecting molecular bases of diseases, designing drugs, refining compounds, and testing drug solubility and effects. Bioinformatics supports various stages of drug development including formulation, crystallization determination, polymer modeling, and testing before human use. Its integration into the pharmaceutical industry supports drug discovery, healthcare advances, and realizing the promises of projects like the Human Genome Project.
INNOVATIVE MEDICINES, TECHNOLOGIES AND APPROACHES FOR IMPROVING PATIENTS' HE...Jing Zang
Despite remarkable scientific and technological achievements during the 20th century, the 21st century has already witnessed additional new and profound changes in all areas of medical science and research, including innovations and discoveries in biology, cellular biology, genomics and proteomics, pharmaceuticals, medical devices, and information technology. This review is an up-date on some of the existing therapies, drug delivery technologies, and approaches that aimed to improve patients’ health care and quality of their life.
This document summarizes a journal article that conducted a systematic review, meta-analysis, and network meta-analysis of studies examining the cardiovascular risks of commonly used fluoroquinolone antibiotics. The review found an association between fluoroquinolone use and increased risks of arrhythmia and cardiovascular mortality. Specifically, the network meta-analysis found that moxifloxacin was associated with the highest risks, while ciprofloxacin had the lowest risks of the antibiotics analyzed. The results provide evidence on the relative cardiac safety risks of different fluoroquinolone antibiotics.
Pluripotent stem cells An in vitro model for nanotoxicityDr. Harish Handral
This document discusses the use of pluripotent stem cells (PSCs) as an in vitro model for assessing nanotoxicity. It notes that existing in vitro and in vivo models have limitations, and that PSCs can differentiate into various cell types and provide a more realistic model that reflects human physiology. PSCs are proposed as a promising alternative platform that could help address current challenges in predicting nanomaterial toxicity and screening new drugs and materials in a reliable and cost-effective way. The review focuses on how induced pluripotent stem cells and embryonic stem cells could be used to establish three-dimensional tissue models for more accurately assessing the hazardous effects of nanomaterials.
Role of bioinformatics and pharmacogenomics in drug discoveryArindam Chakraborty
Bioinformatics and pharmacogenomics can accelerate drug discovery and development processes and reduce costs and timelines. Bioinformatics provides databases and tools to aid in target identification and validation. Pharmacogenomics helps determine individual genetic factors that influence drug responses. Together, they allow more efficient and personalized drug development. While still developing, bioinformatics and pharmacogenomics show potential to support drug design and address barriers like adverse reactions. They may help revive orphan drugs and aid in developing treatments for emerging issues like COVID-19 through drug repurposing informed by human genome interactions.
Integration of knowledge for personalized medicine: a pharmacogenomics case-s...Robert Hoehndorf
This document discusses integrating knowledge from pharmacogenomics databases to enable personalized medicine approaches. It describes using ontologies to integrate data on drugs, diseases, pathways, and genotypes from multiple sources. Queries over this integrated knowledge can discover disease and drug pathways and relationships between genotypes and drugs/diseases. The goal is to identify aberrant pathways underlying a disease and personalized treatment options based on a patient's gene expression profile. Future work includes expanding to interaction networks and experimental validation.
Requesting a complete biosensor system in phyto-sourced drug discovery and de...iosrphr_editor
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
This document outlines the syllabus for the 7th level open and internal competitive examination for Medical Pharmacist conducted by the Public Service Commission of Nepal.
The examination will have two parts - a written exam worth 200 marks and an oral/group test worth 40 marks. The written exam will consist of two parts - a 100 mark multiple choice questions paper and a 100 mark subjective questions paper.
The syllabus covers key topics in pharmacy including development and legislation in Nepal, pharmaceutical analysis, pharmaceutics, pharmacognosy, pharmacology, medicinal chemistry, drug acts and pharmacopoeia, and pharmaceutical care and drug supply management. Sample multiple choice and subjective questions are also provided.
The oral/group test
Bioinformatics role in Pharmaceutical industriesMuzna Kashaf
Bioinformatics plays a key role in the pharmaceutical industry by enabling target identification of diseases, rational drug design, compound refinement, and other processes. It facilitates identifying target diseases and compounds, detecting molecular bases of diseases, designing drugs, refining compounds, and testing drug solubility and effects. Bioinformatics supports various stages of drug development including formulation, crystallization determination, polymer modeling, and testing before human use. Its integration into the pharmaceutical industry supports drug discovery, healthcare advances, and realizing the promises of projects like the Human Genome Project.
INNOVATIVE MEDICINES, TECHNOLOGIES AND APPROACHES FOR IMPROVING PATIENTS' HE...Jing Zang
Despite remarkable scientific and technological achievements during the 20th century, the 21st century has already witnessed additional new and profound changes in all areas of medical science and research, including innovations and discoveries in biology, cellular biology, genomics and proteomics, pharmaceuticals, medical devices, and information technology. This review is an up-date on some of the existing therapies, drug delivery technologies, and approaches that aimed to improve patients’ health care and quality of their life.
This document summarizes a journal article that conducted a systematic review, meta-analysis, and network meta-analysis of studies examining the cardiovascular risks of commonly used fluoroquinolone antibiotics. The review found an association between fluoroquinolone use and increased risks of arrhythmia and cardiovascular mortality. Specifically, the network meta-analysis found that moxifloxacin was associated with the highest risks, while ciprofloxacin had the lowest risks of the antibiotics analyzed. The results provide evidence on the relative cardiac safety risks of different fluoroquinolone antibiotics.
Pluripotent stem cells An in vitro model for nanotoxicityDr. Harish Handral
This document discusses the use of pluripotent stem cells (PSCs) as an in vitro model for assessing nanotoxicity. It notes that existing in vitro and in vivo models have limitations, and that PSCs can differentiate into various cell types and provide a more realistic model that reflects human physiology. PSCs are proposed as a promising alternative platform that could help address current challenges in predicting nanomaterial toxicity and screening new drugs and materials in a reliable and cost-effective way. The review focuses on how induced pluripotent stem cells and embryonic stem cells could be used to establish three-dimensional tissue models for more accurately assessing the hazardous effects of nanomaterials.
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
This document summarizes a seminar presentation on nanogels for drug delivery. It discusses various types of nanogels such as chitosan-polylactic acid nanogels for encapsulating rifampicin. It also describes a study where rifampicin-loaded PLGA nanoparticles were incorporated into mannitol microspheres to enhance macrophage uptake and lung delivery. Overall, the presentation evaluates nanogels as a promising drug delivery system and discusses their ability to provide sustained and targeted drug release.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
Dr. Igor V. Tetko introduces chemoinformatics, which uses informatics methods to solve chemical problems. It involves organizing and analyzing large chemical datasets. Key applications include drug discovery, chemical safety assessments like REACH, and predictive toxicology. Chemoinformatics helps address issues like high drug development costs and testing requirements by predicting properties in silico. The course covers topics like molecular representations, modeling techniques, and the online OCHEM database and modeling platform. Chemoinformatics aims to transform chemical data into knowledge to make better informed decisions.
This document summarizes several approved and investigational nanocarrier drug delivery systems. Doxil and Abraxane are approved liposomal and albumin-bound nanoparticle formulations of doxorubicin and paclitaxel, respectively, for treating various cancers. Coroxane, a microtubule stabilizer delivered using albumin nanoparticles, is in Phase 2 trials for preventing arterial restenosis and treating peripheral artery disease. Nanocarriers can actively or passively target drugs to tumors by mechanisms such as enhanced permeability and retention or by functionalizing the nanoparticle surface with targeting ligands.
Presentation by Piero Olliaro about "Anti-TB Drug R&D: Peculiarities, Pipeline and Initiatives."
Piero Olliaro Bio:
http://www.opensourcepharma.net/participants/piero-olliaro
Conference Agenda (see Day 1, Session 1):
http://www.opensourcepharma.net/agenda.html
An Introduction to Chemoinformatics for the postgraduate students of AgricultureDevakumar Jain
1. Chemoinformatics is the application of informatics methods to solve chemical problems and encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information.
2. It combines aspects of chemistry and computer science to address challenges such as representing and searching large chemical structure databases, predicting molecular properties, and aiding in drug discovery.
3. Chemoinformatics tools and methods have applications in diverse areas including organic synthesis, analytical chemistry, toxicology prediction, and agrochemical discovery.
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
This document provides information on conducting safety pharmacology and toxicology studies for pharmaceuticals. It describes various in vivo and in vitro models used to study the effects of drugs on the central nervous, cardiovascular and respiratory systems as well as metabolism and various disease models. It also outlines general toxicology, genotoxicity, reproductive toxicity and immunotoxicity studies. Both animal and cellular assays are discussed for evaluating potential adverse effects of drug candidates.
The document discusses the process of drug design and development. It begins by defining drugs and their targets at the molecular level. Historically, drugs came from plants and natural products, but now they can be designed rationally through understanding disease processes. The drug design process involves identifying a target, discovering leads, and optimizing candidates through computer modeling and testing before clinical trials. Modern techniques like molecular modeling, virtual screening, and computer-aided design have made drug discovery more efficient, but it remains a long, complex, and expensive process.
Phar 7041 fundamentals of pharmacoepidemiology course outline 2020 21University of Gondar
This document outlines a course on fundamentals of pharmacoepidemiology for postgraduate pharmacy students. The course aims to prepare students to conduct pharmacoepidemiologic research by introducing key concepts and methods. Over the semester, students will learn about study designs, data sources, drug utilization research, pharmacovigilance, and systematic reviews. Assessment methods include assignments, seminar presentations, and a final written exam. Course contents will cover these topics through lectures, discussions, exercises and case studies using recommended textbooks and publications.
The document summarizes GVK BIO Informatics, which provides complete informatics solutions from knowledge management to predictive analytics. It highlights key databases and tools developed by GVK including CTOD (Clinical Trial Outcome Database) and a biomarker database. It also describes GVK's knowledge base development through custom data curation for leading database providers and key publications utilizing GVK's GOSTAR database.
ICIC 2013 New Product Introductions GVK Bio InformaticsDr. Haxel Consult
The document summarizes GVK BIO Informatics, which provides complete informatics solutions from knowledge management to predictive analytics. It highlights key databases and tools developed by GVK including CTOD (Clinical Trial Outcome Database) and biomarker databases containing clinical trial data, small molecules, and biomarkers. The document also discusses GVK's knowledge base development through custom data curation for database providers and lists many publications that have utilized GVK's GOSTAR database.
2011-10-11 Open PHACTS at BioIT World Europeopen_phacts
The document discusses the Innovative Medicines Initiative's Open PHACTS project, which aims to develop robust standards and apply them in a semantic integration platform ("Open Pharmacological Space") to integrate drug discovery data from various public and private sources. The project brings together partners from industry, academia, and non-profits to build an open infrastructure for linking drug discovery knowledge and supporting ongoing research. It outlines the technical approach, priorities, and initial progress on developing exemplar applications and a prototype "lash up" system.
Revolutionizing healthcare and wellness management through systems medicine. The document discusses using systems approaches combining multidimensional omics data with clinical assessments through modeling and experimentation. This enables predictive, preventive, personalized and participatory (P4) medicine. Several research projects applying these approaches to respiratory diseases are mentioned. It also discusses developing standards and infrastructure like tranSMART to facilitate data sharing and collaboration toward implementing systems medicine across Europe.
Revolutionizing Heathcare and Wellness Management through Systems P4 Medicinebrnbarcelona
Revolutionizing healthcare and wellness management through systems medicine approaches like predictive, preventive, personalized and participatory (P4) medicine. The document discusses establishing networks and consortiums across Europe to advance systems medicine through data and knowledge sharing, standardized methods, and integrating multi-omics data with clinical information. The goal is to transition to more proactive, cost-efficient healthcare by better understanding disease at the systems level.
Mel Reichman on Pool Shark’s Cues for More Efficient Drug DiscoveryJean-Claude Bradley
Mel Reichman, senior investigator and director of the LIMR Chemical Genomics Center at the Lankenau Institute for Medical Research presents at the chemistry department at Drexel University on November 12, 2009.
Modern drug discovery by high-throughput screening (HTS) begins with testing hundreds of thousands of compounds in biological assays. The confirmed hit rate for typical HTS is less than 0.5%; therefore, 99.5% of the costs of HTS are for generating null data. Orthogonal convolution of compound libraries (OCL) is 500% more efficient than present HTS practice. The OCL method combines 10 compounds per well. An advantage of this method is that each compound is represented twice in two separately arrayed pools. The potential for the approach to better enable academic centers of excellence to validate medicinally relevant biological targets is discussed.
Ethical considerations on pharmacogenomicsEmilio Mordini
Pharmacogenomics offers the prospect of safer and more effective drugs through a more individualized approach, but also raises new ethical challenges. It promises to fulfill the goal of personalized medicine in an unexpected way. Key ethical issues in pharmacogenomic research include regulatory oversight, informed consent, and access to resulting drugs. In clinical practice, confidentiality, availability of drugs, and clinicians' responsibilities require consideration. Overall, pharmacogenomics research may help protect participants and reduce trial sizes, but clear regulations and procedures are still needed to guide this emerging field.
A radiology report serves as an intermediary between a radiologist and referring clinician for suggesting
appropriate treatment to the patients, aimed at better healthcare management. It is essentially a tool
that assists radiologists in conveying their input to the patients and clinicians regarding positive or negative findings on a case. The objective of this paper is to discuss and propose Radiology Information & Reporting System (RIRS), highlight challenges governing its implementation and suggest way forwards
towards its effective implementation across the public sector tertiary care institutions of Pakistan. In the end, it is concluded that the proposed RIRS would potentially offer enormous benefits in terms of cost
savings, reporting accuracy, faster processing and operational efficiency as opposed to the conventionally available manual radiology reporting procedures and systems.
Allometry scaling is used to predict pharmacokinetic parameters such as volume of distribution, clearance, and half-life in humans based on animal data. It involves plotting parameters against body weight on a log-log scale to determine relationships. Two approaches for interspecies scaling are physiological models using organ sizes and rates, and empirical allometric methods. Accurate prediction requires data from multiple animal species, though some use fewer. The goal of allometric scaling is to safely estimate first human doses during drug development.
The document discusses online resources that can support open drug discovery systems. It outlines how pharmaceutical companies spend billions annually on R&D and how public domain data from sources like literature, patents and databases could provide high value. However, such data is difficult to integrate and navigate due to a lack of standards and interoperability between sources. The Open PHACTS project aims to address this by developing standards to semantically integrate drug discovery data from public and private sources.
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
This document summarizes a seminar presentation on nanogels for drug delivery. It discusses various types of nanogels such as chitosan-polylactic acid nanogels for encapsulating rifampicin. It also describes a study where rifampicin-loaded PLGA nanoparticles were incorporated into mannitol microspheres to enhance macrophage uptake and lung delivery. Overall, the presentation evaluates nanogels as a promising drug delivery system and discusses their ability to provide sustained and targeted drug release.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
Dr. Igor V. Tetko introduces chemoinformatics, which uses informatics methods to solve chemical problems. It involves organizing and analyzing large chemical datasets. Key applications include drug discovery, chemical safety assessments like REACH, and predictive toxicology. Chemoinformatics helps address issues like high drug development costs and testing requirements by predicting properties in silico. The course covers topics like molecular representations, modeling techniques, and the online OCHEM database and modeling platform. Chemoinformatics aims to transform chemical data into knowledge to make better informed decisions.
This document summarizes several approved and investigational nanocarrier drug delivery systems. Doxil and Abraxane are approved liposomal and albumin-bound nanoparticle formulations of doxorubicin and paclitaxel, respectively, for treating various cancers. Coroxane, a microtubule stabilizer delivered using albumin nanoparticles, is in Phase 2 trials for preventing arterial restenosis and treating peripheral artery disease. Nanocarriers can actively or passively target drugs to tumors by mechanisms such as enhanced permeability and retention or by functionalizing the nanoparticle surface with targeting ligands.
Presentation by Piero Olliaro about "Anti-TB Drug R&D: Peculiarities, Pipeline and Initiatives."
Piero Olliaro Bio:
http://www.opensourcepharma.net/participants/piero-olliaro
Conference Agenda (see Day 1, Session 1):
http://www.opensourcepharma.net/agenda.html
An Introduction to Chemoinformatics for the postgraduate students of AgricultureDevakumar Jain
1. Chemoinformatics is the application of informatics methods to solve chemical problems and encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information.
2. It combines aspects of chemistry and computer science to address challenges such as representing and searching large chemical structure databases, predicting molecular properties, and aiding in drug discovery.
3. Chemoinformatics tools and methods have applications in diverse areas including organic synthesis, analytical chemistry, toxicology prediction, and agrochemical discovery.
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
This document provides information on conducting safety pharmacology and toxicology studies for pharmaceuticals. It describes various in vivo and in vitro models used to study the effects of drugs on the central nervous, cardiovascular and respiratory systems as well as metabolism and various disease models. It also outlines general toxicology, genotoxicity, reproductive toxicity and immunotoxicity studies. Both animal and cellular assays are discussed for evaluating potential adverse effects of drug candidates.
The document discusses the process of drug design and development. It begins by defining drugs and their targets at the molecular level. Historically, drugs came from plants and natural products, but now they can be designed rationally through understanding disease processes. The drug design process involves identifying a target, discovering leads, and optimizing candidates through computer modeling and testing before clinical trials. Modern techniques like molecular modeling, virtual screening, and computer-aided design have made drug discovery more efficient, but it remains a long, complex, and expensive process.
Phar 7041 fundamentals of pharmacoepidemiology course outline 2020 21University of Gondar
This document outlines a course on fundamentals of pharmacoepidemiology for postgraduate pharmacy students. The course aims to prepare students to conduct pharmacoepidemiologic research by introducing key concepts and methods. Over the semester, students will learn about study designs, data sources, drug utilization research, pharmacovigilance, and systematic reviews. Assessment methods include assignments, seminar presentations, and a final written exam. Course contents will cover these topics through lectures, discussions, exercises and case studies using recommended textbooks and publications.
The document summarizes GVK BIO Informatics, which provides complete informatics solutions from knowledge management to predictive analytics. It highlights key databases and tools developed by GVK including CTOD (Clinical Trial Outcome Database) and a biomarker database. It also describes GVK's knowledge base development through custom data curation for leading database providers and key publications utilizing GVK's GOSTAR database.
ICIC 2013 New Product Introductions GVK Bio InformaticsDr. Haxel Consult
The document summarizes GVK BIO Informatics, which provides complete informatics solutions from knowledge management to predictive analytics. It highlights key databases and tools developed by GVK including CTOD (Clinical Trial Outcome Database) and biomarker databases containing clinical trial data, small molecules, and biomarkers. The document also discusses GVK's knowledge base development through custom data curation for database providers and lists many publications that have utilized GVK's GOSTAR database.
2011-10-11 Open PHACTS at BioIT World Europeopen_phacts
The document discusses the Innovative Medicines Initiative's Open PHACTS project, which aims to develop robust standards and apply them in a semantic integration platform ("Open Pharmacological Space") to integrate drug discovery data from various public and private sources. The project brings together partners from industry, academia, and non-profits to build an open infrastructure for linking drug discovery knowledge and supporting ongoing research. It outlines the technical approach, priorities, and initial progress on developing exemplar applications and a prototype "lash up" system.
Revolutionizing healthcare and wellness management through systems medicine. The document discusses using systems approaches combining multidimensional omics data with clinical assessments through modeling and experimentation. This enables predictive, preventive, personalized and participatory (P4) medicine. Several research projects applying these approaches to respiratory diseases are mentioned. It also discusses developing standards and infrastructure like tranSMART to facilitate data sharing and collaboration toward implementing systems medicine across Europe.
Revolutionizing Heathcare and Wellness Management through Systems P4 Medicinebrnbarcelona
Revolutionizing healthcare and wellness management through systems medicine approaches like predictive, preventive, personalized and participatory (P4) medicine. The document discusses establishing networks and consortiums across Europe to advance systems medicine through data and knowledge sharing, standardized methods, and integrating multi-omics data with clinical information. The goal is to transition to more proactive, cost-efficient healthcare by better understanding disease at the systems level.
Mel Reichman on Pool Shark’s Cues for More Efficient Drug DiscoveryJean-Claude Bradley
Mel Reichman, senior investigator and director of the LIMR Chemical Genomics Center at the Lankenau Institute for Medical Research presents at the chemistry department at Drexel University on November 12, 2009.
Modern drug discovery by high-throughput screening (HTS) begins with testing hundreds of thousands of compounds in biological assays. The confirmed hit rate for typical HTS is less than 0.5%; therefore, 99.5% of the costs of HTS are for generating null data. Orthogonal convolution of compound libraries (OCL) is 500% more efficient than present HTS practice. The OCL method combines 10 compounds per well. An advantage of this method is that each compound is represented twice in two separately arrayed pools. The potential for the approach to better enable academic centers of excellence to validate medicinally relevant biological targets is discussed.
Ethical considerations on pharmacogenomicsEmilio Mordini
Pharmacogenomics offers the prospect of safer and more effective drugs through a more individualized approach, but also raises new ethical challenges. It promises to fulfill the goal of personalized medicine in an unexpected way. Key ethical issues in pharmacogenomic research include regulatory oversight, informed consent, and access to resulting drugs. In clinical practice, confidentiality, availability of drugs, and clinicians' responsibilities require consideration. Overall, pharmacogenomics research may help protect participants and reduce trial sizes, but clear regulations and procedures are still needed to guide this emerging field.
A radiology report serves as an intermediary between a radiologist and referring clinician for suggesting
appropriate treatment to the patients, aimed at better healthcare management. It is essentially a tool
that assists radiologists in conveying their input to the patients and clinicians regarding positive or negative findings on a case. The objective of this paper is to discuss and propose Radiology Information & Reporting System (RIRS), highlight challenges governing its implementation and suggest way forwards
towards its effective implementation across the public sector tertiary care institutions of Pakistan. In the end, it is concluded that the proposed RIRS would potentially offer enormous benefits in terms of cost
savings, reporting accuracy, faster processing and operational efficiency as opposed to the conventionally available manual radiology reporting procedures and systems.
Allometry scaling is used to predict pharmacokinetic parameters such as volume of distribution, clearance, and half-life in humans based on animal data. It involves plotting parameters against body weight on a log-log scale to determine relationships. Two approaches for interspecies scaling are physiological models using organ sizes and rates, and empirical allometric methods. Accurate prediction requires data from multiple animal species, though some use fewer. The goal of allometric scaling is to safely estimate first human doses during drug development.
The document discusses online resources that can support open drug discovery systems. It outlines how pharmaceutical companies spend billions annually on R&D and how public domain data from sources like literature, patents and databases could provide high value. However, such data is difficult to integrate and navigate due to a lack of standards and interoperability between sources. The Open PHACTS project aims to address this by developing standards to semantically integrate drug discovery data from public and private sources.
This resume summarizes Sean Ekins' experience and qualifications. He has over 17 years of experience in drug discovery from large pharmaceutical companies to small biotechs. He is an expert in computational and in vitro tools for accelerating drug discovery and reducing compound attrition. He has held leadership roles at several companies and currently works as an independent consultant advising various organizations. He is also an active researcher and writer with over 200 publications.
Micky Tortorella is an experienced pharmaceutical scientist who has worked in drug discovery for over 15 years. He co-founded Legion Pharmaceuticals in 2019 and serves as their Chief Scientific Officer. In this role, he leads drug discovery efforts focusing on modulating the tissue microenvironment to treat diseases. Previously, he held leadership roles at Pfizer and the Guangzhou Institutes of Biomedicine and Health, where he established successful drug discovery programs. Micky has over 70 publications and 30 patents regarding targets for inflammatory diseases and osteoarthritis.
Quantifying the content of biomedical semantic resources as a core for drug d...Syed Muhammad Ali Hasnain
The biomedical research community is providing large-scale data sources to enable knowledge discovery from the data alone, or from novel scientific experiments in combination with the existing knowledge.
Increasingly semantic Web technologies are being developed and used including ontologies, triple stores and combinations thereof.
The amount of data is constantly increasing as well as the complexity of data.
Since the data sources are publicly available, the amount of content can be derived giving an overview on the accessible content but also on the state of the data representation in comparison to the existing content.
For a better understanding of the existing data resources, i.e.\ judgments on the distribution of data triples across concepts, data types and primary providers, we have performed a comprehensive analysis which delivers an overview on the accessible content for semantic Web solutions.
It can be derived that the information related to genes, proteins and chemical entities form the center, whereas the content related to diseases and pathways forms a smaller portion.
Further data relates to dietary content and specific questions such as cancer prevention and toxicological effects of drugs.
Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology. It enables the discovery of new biological insights and unifying principles in biology through the merging of these disciplines. There are three main sub-disciplines: developing algorithms and statistics for analyzing large datasets, analyzing various types of biological data like sequences and structures, and developing tools for accessing and managing information.
Talk at Yale University April 26th 2011: Applying Computational Modelsfor To...Sean Ekins
The document discusses applying computational models to problems in toxicology, drug discovery, and beyond. It summarizes recent work using machine learning models and other in silico techniques to predict drug-induced liver injury (DILI) and interactions with transporters like hOCTN2. Models were able to classify compounds as DILI-positive or negative with over 75% accuracy when tested on external datasets. The techniques discussed could help prioritize compounds for further testing and filter libraries to avoid reactive or toxic features.
Ontologies for Semantic Normalization of Immunological DataYannick Pouliot
This document discusses using ontologies to semantically normalize immunological data from the Human Immune Profiling Consortium (HIPC). 57 ontologies covering domains like anatomy, disease, pathways were evaluated. Text from HIPC datasets and protocols was annotated using these ontologies, with the NCI Thesaurus, Medical Subject Headings, and Gene Ontology mapping to the most terms. Many failures were due to missing commercial reagent terms. The conclusions are that ImmPort, the HIPC data repository, could adopt ontology-based encoding with additions to ontologies and text pre-processing.
This document discusses databases that define the druggable proteome - the portion of the human proteome that can bind small molecules with sufficient affinity for modulating protein function. Four databases - ChEMBL, BindingDB, DrugBank, and IUPHAR/BPS Guide to PHARMACOLOGY - provide evidence-supported links between human proteins and drug targets. Their intersection identifies ~490 proteins (13% of the union of targets) as the most precisely defined druggable proteome. Comparative analyses examine distributions of targets by function and other attributes. Initiatives aim to expand knowledge of currently unannotated but potentially druggable protein families to broaden therapeutic opportunities.
Cheminformatics plays a key role in modern drug discovery by helping chemists organize and analyze the vast amounts of chemical data being produced. It combines fields like chemistry, biology, and informatics to transform data into knowledge. Specifically, cheminformatics aids in tasks like identifying drug targets, finding lead compounds, optimizing leads, and conducting pre-clinical trials through methods such as high-throughput screening, structure-activity modeling, and predictive toxicity analysis. It also provides tools for tasks like drawing and searching chemical structures in databases.
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...Human Variome Project
The success of whole exome sequencing (WES) for highly heterogeneous disorders, such as mitochondrial disease, is limited by substantial technical and bioinformatics challenges to correctly identify and prioritize the extensive number of sequence variants present in each patient. The likelihood of success can be greatly improved if a large cohort of patient data is assembled in which sequence variants can be systematically analysed, annotated, and interpreted relative to known phenotype. This effort has engaged and united more than 100 international mitochondrial clinicians, researchers, and bioinformaticians in the Mitochondrial Disease Sequence Data Resource (MSeqDR) consortium that formed in June 2012 to identify and prioritize the specific WES data analysis needs of the global mitochondrial disease community. Through regular web-based meetings, we have familiarized ourselves with existing strengths and gaps facing integration of MSeqDR with public resources, as well as the major practical, technical, and ethical challenges that must be overcome to create a sustainable data resource. We have now moved forward toward our common goal by establishing a central data resource (http://mseqdr.org/) that has both public access and secure web-based features that allow the coherent compilation, organization, annotation, and analysis of WES and mtDNA genome data sets generated in both clinical- and research-based settings of suspected mitochondrial disease patients. The most important aims of the MSeqDR consortium are summarized in the MSeqDR portal within the Consortium overview sections. Consortium participants are organized in 3 working groups that include (1) Technology and Bioinformatics; (2) Phenotyping, databasing, IRB concerns and access; and (3) Mitochondrial DNA specific concerns. The online MSeqDR resource is organized into discrete sections to facilitate data deposition and common reannotation, data visualization, data set mining, and access management. With the support of the United Mitochondrial Disease Foundation (UMDF) and the NINDS/NICHD U54 supported North American Mitochondrial Disease Consortium (NAMDC), the MSeqDR prototype has been built. Current major components include common data upload and reannotation using a novel HBCR based annotation tool that has also been made publicly available through the website, MSeqDR GBrowse that allows ready visualization of all public and MSeqDR specific data including labspecific aggregate data visualization tracks, MSeqDR-LSDB instance of nearly 1250 mitochondrial disease and mitochodnrial localized genes that is based on the Locus Specific Database model, exome data set mining in individuals or families using the GEM.app tool, and Account & Access Management. Within MSeqDR GBrowse it is now possible to explore data derived from MitoMap, HmtDB, ClinVar, UCSC-NumtS, ENCODE, 1000 genomes, and many other resources that bioinformaticians recruited to the project are organizing.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
1. University of Helsinki
Université Paris Diderot
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
Alexandre Borrel
Defence of doctoral dissertation
26 May 2016
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973 1
2. University of Helsinki
Université Paris Diderot 26-05-20162
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Outlines
Year 2
Year 1
3. University of Helsinki
Université Paris Diderot 26-05-20163
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Background
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
4. University of Helsinki
Université Paris Diderot 26-05-20164
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Background
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
5. University of Helsinki
Université Paris Diderot 26-05-20165
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Background
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
6. University of Helsinki
Université Paris Diderot 26-05-20166
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Structural data
-7.265 20.187 20.701
-4.182 20.865 18.600
Structure of the biological macromolecules (protein) at an atomic level
3D coordinates (x, y, z)
element (oxygen, nitrogen, carbon)
-6.288 20.665 18.600
-4.288 21.665 15.600
-4.188 20.665 18.600
-3.089 20.665 18.600
-6.288 21.685 18.600
-6.288 20.665 18.600
7. University of Helsinki
Université Paris Diderot 26-05-20167
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Issues with structural data
110 288 proteins structures (1)
(May 2016)
(1) Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., et al. (2000). Nucleic Acids Res. 28: 235–242.
(2) Fersht, A.R. (2008) Nat. Rev. Mol. Cell Biol. 9: 650–654.
(3) Tari, L.W. (2012). Structure-Based Drug Discovery (Totowa, NJ: Humana Press).
Drug discovery (2-3):
• Rationalize drug discovery
• Open new trails of development
• Reduce the cost and the time
• …
8. University of Helsinki
Université Paris Diderot 26-05-20168
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Background
Development of Computational Methods to
Predict Protein Pocket Druggability and
Profile Ligands using Structural Data
9. University of Helsinki
Université Paris Diderot 26-05-20169
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Predict the recognition
Holy grail: predict recognition between a ligand and a target
using only protein and ligand structure.
Computational
methodsTarget structure
Ligand/drug structure
10. University of Helsinki
Université Paris Diderot 26-05-201610
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Protein-ligand recognition
“Lock-and-key”, Emil Fischer in 1894
(60 years before the first 3D structure)
Fischer, E. Einfluss. Ber. Dtsch. Chem. Ges. 1894, 27, 2985–2993.
Complementarity of shapes between a ligand (key) and a protein (lock).
11. University of Helsinki
Université Paris Diderot 26-05-201611
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Protein-ligand recognition
Koshland, D.E. (1958). Proc. Natl. Acad. Sci. U. S. A. 44: 98–104.
“Induced-fit model” Daniel Koshland, 1958
Proteins and ligands adapt their conformations for the recognition.
12. University of Helsinki
Université Paris Diderot 26-05-201612
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Challenges
Many factors influence the protein-ligand recognition such as molecular
interactions, environment (i.e. solvent), …
Water
~4.6 water molecules by binding site (1)
(1) Lu, Y., Wang, R., Yang, C.-Y., and Wang, S. (2007). J. Chem. Inf. Model. 47: 668–675.
H-bond
π-π
hydrophobe
Challenges: model all phenomena which explain the recognition.
13. University of Helsinki
Université Paris Diderot 26-05-201613
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Aims of the thesis
Develop computational methods useful for the ligand profiling and contributing in
the improvement of the modeling of the protein-ligand recognition.
Data analysis
Pocket / target space
Medicinal chemistry
Molecular modeling
?
14. University of Helsinki
Université Paris Diderot 26-05-201614
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Druggability model
Year 2
Year 1
Recognition
Structural data
Protein
target
http://phdcomics.com/comics.php
15. University of Helsinki
Université Paris Diderot 26-05-201615
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Binding sites
A binding site will refer to the atoms of the amino acid at interacting distances (4 to
6 Å) of a bound ligand, and present at the surface of the binding region.
Cavity Channel Protein-protein interphase
16. University of Helsinki
Université Paris Diderot 26-05-201616
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Drug-like molecules
Drug-like: compound with acceptable Absorption, Distribution, Metabolism,
and Excretion – toxicity properties to become orally bioavailable drug (1-2).
Rules of five (from 2 200 compounds in the United States Adopted Names
directory) in 1997 (1):
(1) Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (2001). Adv. Drug Deliv. Rev. 46: 3–26.
(2) Tian, S., Wang, J., Li, Y., Li, D., Xu, L., and Hou, T. (2015). Adv. Drug Deliv. Rev. 86: 2–10.
• Molecular weight ≤ 500 Da
• LogP ≤ 5
• H-bond acceptors ≤ 10
• H-bond donors ≤ 5
Ligand drug-like: Bisindolylmaleimide Inhibitor
17. University of Helsinki
Université Paris Diderot 26-05-201617
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Drug-like molecules
“Rules of five” are important to
prioritize/rationalize the chemical space for
virtual screening on the first drug discovery
step (12 billion accessible molecules) (1-2)
(1) Hann, M.M., and Oprea, T.I. (2004). Curr. Opin. Chem. Biol. 8: 255–263.
(2) Ursu, O., Rayan, A., Goldblum, A., and Oprea, T.I. (2011). Rev. Comput. Mol. Sci. 1: 760–781.
(3) Perola, E., Herman, L., and Weiss, J. (2012). J. Chem. Inf. Model. 52: 1027–1038.
(4) Hopkins, A.A.L., and Groom, C.R.C. (2002). The druggable genome. Nat. Rev. Drug Discov. 1: 727–730.
Druggability: “…defined as the ability of a target to bind a
drug-like molecule with a therapeutically useful level of
affinity.” (3-4)
18. University of Helsinki
Université Paris Diderot 26-05-201618
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Protein druggability
Similarly to the rules of five to rationalize the ligand space, druggability models
are developed to rationalize the target space
Statistical model
Druggable ?
19. University of Helsinki
Université Paris Diderot 26-05-201619
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
1. Pocket estimation
Prediction of
druggability (from
properties of the
know druggable
pockets)
2. Model pockets 3. Statistical model
A E
TR
Protein druggability
Similarly to the rules of five to rationalize the ligand space, druggability models
are developed to rationalize the target space
20. University of Helsinki
Université Paris Diderot 26-05-201620
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Challenges
Pocket estimation
Availability
« ...different pocket detection methods can assign different sizes and/or numbers
of pockets for the same structure. »
(1) Gao, M., & Skolnick, J. (2013). Bioinformatics (Oxford, England), 29(5), 597–604
Hajduk’s model
SCREEN
MAPPOD
SiteMap
DLID
Huang’s model
Huang’s model
Fpocket
DrugPred
DoGSite-Scorer
CAVITY-Score
DrugFEATURE
FTMap
Druggability models are depending on a pocket estimation method, which limit their
availability for pocket differently estimated using visual expertize for example.
21. University of Helsinki
Université Paris Diderot 26-05-201621
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Pockets estimated on a same binding site
have a weak average overlap (%)
• Prox - Fpocket = 30 % (±14 %)
• Prox - DoGSite = 28 % (± 14 %)
• Fpocket- DoGSite = 30 % (± 16 %)
Step 1: Pocket estimation
Develop a druggability model which considers several pocket estimations
We used three pocket estimation methods:
• Ligand proximity (Prox)
• Geometric approach (Fpocket) (1)
• Energetic approach (DoGSite) (2)
(1) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168.
(2) Volkamer, A., Griewel, A., Grombacher, T., and Rarey, M. (2010). J. Chem. Inf. Model. 50: 2041–2052.
22. University of Helsinki
Université Paris Diderot 26-05-201622
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Step 2: Pocket modeling
Pocket are modeled using a set of 52 descriptors implemented
Composition (1-2)
(atomic and residues) Hydrophobicity (2-4) Geometry (5)
(1) Milletti, F., and Vulpetti, A. (2010). J. Chem. Inf. Model. 50: 1418–1431.
(2) Kyte, J., and Doolittle, R.F. (1982).J. Mol. Biol. 157: 105–132.
(3) Eyrisch, S., and Helms, V. (2007). J. Med. Chem. 50: 3457–3464.
(4) Hubbard, SJ and Thornton, J. (1992). NACCESS version 2.1.1.
(5) Petitjean, M. (1992). J. Chem. Inf. Model. 32: 331–337.
G A
DY Aromatic
Polar
Charged
23. University of Helsinki
Université Paris Diderot 26-05-201623
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Principal component analysis for pocket sets estimated differently (Prox, Fpocket
and DoGSite) using a unique dataset of 111 binding sites (NRDLD) (1).
Step 3: Pocket spaces
(1) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842.
24. University of Helsinki
Université Paris Diderot 26-05-201624
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Druggable pockets
Druggable and less druggable pocket
spaces are separated in the projection.
Volume Polarity
Hydrophobicity
Aromaticity
25. University of Helsinki
Université Paris Diderot 26-05-201625
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Training phase
Parsimonious linear discriminant analysis
models (internal validation cross
validation 10-folds)
Selection of the models performing on
different pockets sets estimated differently
Consensus model (average of 7 linear
discriminate analysis models)
26. University of Helsinki
Université Paris Diderot 26-05-201626
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
External validation
+ 10% in accuracy
+ 0.20 in MCC
Matthew’s Coefficient Correlation (MCC)
(11) Desaphy, J., Azdimousa, K., Kellenberger, E., and Rognan, D. (2012). J. Chem. Inf. Model. 52: 2287–2299.
(14) Krasowski, A., Muthas, D., Sarkar, A., Schmitt, S., and Brenk, R. (2011). J. Chem. Inf. Model. 51: 2829–2842.
(10) Halgren, T. a (2009). J. Chem. Inf. Model. 49: 377–389.
(12) Guilloux, V. Le, Schmidtke, P., and Tuffery, P. (2009). BMC Bioinformatics 10: 168.
(15) Volkamer, A., Kuhn, D., Rippmann, F., and Rarey, M. (2012). Bioinformatics 28: 2074–2075.
27. University of Helsinki
Université Paris Diderot 26-05-201627
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Output of PockDrug model
Geometry Hydrophobicity Aromaticity
Acetylcholinesterase
complexed with Huprine
0.82 +/- 0.09
Druggable probability
(Average)
Confidence
(Standard deviation)
PockDrug combines three pocket properties
i.e. geometry, hydrophobicity and the
aromaticity
28. University of Helsinki
Université Paris Diderot 26-05-201628
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
PockDrug model
Druggability model developed, named PockDrug:
• Robust for different pocket estimation methods
• Exhibits better performances that other models in the literature
• Define important global properties for the recognition (hydrophobicity,
aromaticity and geometry)
Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a
model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem.
Inf. Model. 55: 882–895.
29. University of Helsinki
Université Paris Diderot 26-05-201629
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
PockDrug-Server
Druggability model developed, named PockDrug:
• Robust for different pocket estimation methods
• Exhibits better performances that other models in the literature
• Define important global properties for the recognition (hydrophobicity,
aromaticity and geometry)
http://pockdrug.rpbs.univ-paris-diderot.fr/
Borrel, A., Regad, L., Xhaard, H.G., Petitjean, M., and Camproux, A.-C. (2015). PockDrug: a
model for predicting pocket druggability that overcomes pocket estimation uncertainties. J. Chem.
Inf. Model. 55: 882–895.
Hussein, H.A*., Borrel, A.*, Geneix, C., Petitjean, M., Regad, L., and Camproux, A.-C. (2015).
PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.
Nucleic Acids Res. 1–7.
30. University of Helsinki
Université Paris Diderot 26-05-201630
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
PockDrug-Server
31. University of Helsinki
Université Paris Diderot 26-05-201631
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
To the ligand profiling
Which profile of ligands?
32. University of Helsinki
Université Paris Diderot 26-05-201632
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Local structural replacements
2 year
1 year
Recognition
Structural data
Druggability
Binding site
Ligand
http://phdcomics.com/comics.php
33. University of Helsinki
Université Paris Diderot 26-05-201633
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Drug optimization
Hann, M.M. (2011). Medchemcomm 2: 349–355.
Develop series of chemical
modifications to modulate drug
properties.
34. University of Helsinki
Université Paris Diderot 26-05-201634
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Bioisosterism
(1) Brown, N. (2014). Mol. Inform. 33: 458–462.
(2) Southall, N.T., and Ajay (2006). J. Med. Chem. 49: 2103–2109
Example of bioisosteres from the
kinase patent space (2)
“Bioisosterism is the concept of similarity between functional groups or scaffolds
in molecules that exhibit the same shape in terms of their potential biological
interactions.”(1)
Which replacements are possible?
35. University of Helsinki
Université Paris Diderot
Hypothesis: From two superimposed homologue
proteins, chemical groups which occupy the same
space may be bioisosteric replacements.
26-05-201635
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Local structure replacements
Local structural replacement (LSR)
Computational methods to extract the local
structural replacements
36. University of Helsinki
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Study case: phosphate
• Attractive target for therapeutic development (1).
• 30% of the cellular proteins are phosphoproteins
• Phosphate group is charged at biological pH, poorly
permeable (2).
ATP
Phosphate groups
(1) Cohen, P. (2000). Trends Biochem. Sci. 25: 596–601.
(2) Smith, F.W., Mudge, S.R., Rae, A.L., and Glassop, D. (2003). Plant Soil 248: 71–83.
37. University of Helsinki
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Computational workflow
38. University of Helsinki
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Computational workflow
15 819 phosphate replacements
39. University of Helsinki
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Hierarchical organization
• Local structure containing
• 16 Protein family (KS = Kinase)
• 70 clusters (30% of identity sequences)
• LGD (Ligand)
• LSR (Local Structure Replacements)
• BS (Binding site, 4.5 Å)
40. University of Helsinki
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Hierarchical organization
41. University of Helsinki
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Phosphate is not replaced by the ligand but by the protein (flexible loop)
PDB code: 3JZI – 1DV2
These observations are not quantitative in terms of affinity
Mechanisms of replacements
42. University of Helsinki
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Congener series
U-shape replacements, found in different protein families.
Considering the congener series in different families, when the U-shape is
destabilized the binding affinity decreases.
43. University of Helsinki
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Hydrophobic replacements, favour hydrophobic
contacts in binding site.
Miscellaneous replacements
Positively charged replacement is
surprising considering that the phosphate
groups are negatively charged.
44. University of Helsinki
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Conclusion (LSR)
• 15 819 phosphate replacements
• Organization based on target and type of replacements
• Discussion of some mechanisms for the recognition
A. Borrel*; Y. Zhang*; L. Ghemtio; L. Regad; G. Boije af Gennäs; A.-C. Camproux; J. Yli-
Kauhaluoma; H. Xhaard. Structural replacements of phosphate groups in the Protein Data Bank
(Manuscript)
Perspective:
• Workflow is fully customizable
45. University of Helsinki
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Molecular interactions
Year 2
Year 1
Recognition
Structural data
Molecular
interactions
Druggability
Binding sites
Ligand
replacements
http://phdcomics.com/comics.php
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Molecular interactions
H-bond
π-π
“Intramolecular attractions or repulsions between atoms that are not directly
linked to each other, affecting the thermodynamic stability of the chemical species
concerned.” (IUPAC)
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Example of H-bond
• Geometry (180°), distance criteria
• Directionality
• Partial charges
Hydrogen bond is a non-bonded interaction where two electronegative atoms or
group of atoms share a hydrogen.
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Challenges
49. University of Helsinki
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Challenges
50. University of Helsinki
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Challenges
Distance (Å)
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Challenges
However, some type of interactions i.e. salt-bridges combines different energy
types, influencing the geometry and the strength. Also the environment or/and
interaction network influences the binding interaction.
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Hypothesis of ionic groups
PDB (~100 000 proteins structures), important diversity of interactions.
Data-mining to investigate/model the neighborhood of these interactions.
Six different ionic groups have characterized, only primary amine is presented.
Qualitative and quantitative description of ionic interactions in the binding
site based on their environments.
53. University of Helsinki
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Neighborhood
1 632 in protein-ligand interactions
154 979 in intra-protein interactions
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Neighborhood
Neighborhood: group of 4 first atoms close to the primary amine
Oxygen in carboxylate (Oox)
Oxygen in water molecules (Ow)
Oxygen in hydroxyl (Oh)
Oxygen carbonyl (Oc)
Nitrogen in amide (Nam)
Nitrogen imidazole (Nim)
Nitrogen in guanidinium (Ngu)
Nitrogen in lysine (NaI)
Carbon sp2 and nitrogen sp2 (aromatic) (Car)
Other carbon or sulfur atom (Xot)
Oh
Oh
Oox
Oox
Ow
Ow
Oh
Oox
Oh
Nim
Oc
55. University of Helsinki
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Neighborhood (first neighbors)
Combination of the four first neighbors, distance is not considered
Environments including a carboxylate (Oox)
Quantitative analysis (50% of
primary amines are ionized with
a carboxylate)
Preferential environments and also missing and poor represented environments.
56. University of Helsinki
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Modeling the environment
Neighbors
1 2 3 4
Type
Oox 234 400 320 123
Ow 789 457 690 389
Oh 589 673 590 499
…
Contingency table by position
2D projections
Correspondence analysis: dependency between the neighbor and the atom type.
57. University of Helsinki
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Modeling the environment
Two environments are clearly different in terms of neighbors and closest atoms.
+++ Carboxylate (Oox)
++ Hydroxyl (Oh)
+++ water molecules (Ow)
++ Oxygen carbonyl (Oc)
++ Hydroxyl (Oh)
58. University of Helsinki
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Interaction modeling
• Similar conclusions for intra-protein interactions and protein-ligand
interaction.
• Acidic and basic groups are interacting with a counter ion in 45-54% of cases.
When functional groups of ionizable character are accounted (Oh, Ow) this
number raise to 71%-100% of molecular complexes depending the functional
group at hand.
• Water molecules play a key role in the stabilization of polar groups
especially in absence of salt bridges.
59. University of Helsinki
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Perspectives
Perspectives
• Docking scoring function, i.e. function which considers environment of ligand
decomposition substructure.
• Quantify the preference or missing environments of the interactions
A. Borrel; A.-C. Camproux; H. Xhaard. Interactions of amine, carboxylic acid, imidazole, and
guanidinium groups in proteins and protein-ligand complexes (Manuscript)
60. University of Helsinki
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Outlines
2 year
1 year
Recognition
Structural data
Druggability
Binding sites
Ligand
replacements
Interaction
61. University of Helsinki
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And water molecules
2 year
1 year
Recognition
Structural data
Water
molecules
Ligand
replacements
Druggability
Binding sites
Interactions
62. University of Helsinki
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Water molecules
• Poorly crystallized
• Present only at very high resolution
(< 1.5 Å)
Method to position water molecules
63. University of Helsinki
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In development
Geometric based approach to position water molecules.
Preliminary results: 80%
of the water molecules are
well repositioned.
64. University of Helsinki
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Conclusion
2 year
1 year
Recognition
Structural data
Druggability
Binding site
Ligand
replacements
Interaction
http://phdcomics.com/comics.php
65. University of Helsinki
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Conclusion
Develop computational methods useful for the ligand profiling and contributing in
the improvement of the modeling of the protein-ligand recognition.
Data analysis
Pocket / target space
Medicinal chemistry
Molecular modeling
?
66. University of Helsinki
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Conclusion
Binding sites and the targets:
druggability model
67. University of Helsinki
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Conclusion
Ligands: Methods for local
structure replacements
26-05-2016 67
Binding sites and the targets:
druggability model
68. University of Helsinki
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Conclusion
Interactions: Methods for
local replacements
Binding sites and the targets:
druggability model
Ligands: Methods for local
structure replacements
69. University of Helsinki
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Conclusion
Interactions: Methods for
local replacements
Environment: positioning of
water molecules
Binding sites and the targets:
druggability model
Ligands: Methods for local
structure replacements
70. University of Helsinki
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H. Abi Hussein
Dr. K. Audouze
I. Allam
Dr. A. Badel
H. Borges
J. Bécot
Dr. D. Flatters
C. Geneix
Dr. M. Kuenemann
Dr. D. Lagorce
L. Legall
Dr. M. Louet
Dr. M. Miteva
Dr. M. Petitjean
I. Rasolohery
Dr. L. Regad
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Acknowledgements
Laboratory MTi
(Paris Diderot)
Division of pharmaceutical
chemistry and technology
(Helsinki)
Members of the jury
Dr. O. Sperandio
Prof. O. Taboureau
I. Toussies
D. Triki
Dr. B. Villoutreix
Dr. B. Zarzycka
D. Brandao
K. Culotta
Dr. L. Ghemtio
L. Kharu
A. Legehar
Dr. A. Magarkar
M. Rinne
M. Stepniewski
V. Subramanian
A. Turku
F. Vedovi
Dr. G. Wissel
Dr. Y. Zhang
Dr. N. Brown
Prof. A.C. Camproux
Prof. C. Etchebest
Prof. B. Offmann
Prof. A. Poso
Dr. H. Xhaard
Prof. J. Yli-Kauhaluoma
71. University of Helsinki
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Merci
Thank you
Kiitos
72. University of Helsinki
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Annexes
73. University of Helsinki
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Classification
Dependency of the protein target = classification of different local structure
replacements.
Meanwell, N.A.N.N. a (2011). J. Med. Chem. 54: 2529–2591.
Angiotensin II receptor antagonist analogs
cPLAA2α inhibitor analogs
+ + + - - -
74. University of Helsinki
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Congener series
“One of two or more substances related to each other by origin, structure, or
function.” (IUPAC)
Shin, Y., Chen, W., Habel, J., Duckett, D., Ling, Y.Y., Koenig, M., et al. (2009). Bioorganic Med. Chem. Lett. 19: 3344–3347.
A group change and the
affinity (IC50)
75. University of Helsinki
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Drug discovery
Drug discovery: the process by which new candidate medications are discovered
- Target identification
- Affinity
- Drug candidate
- Lead selection
- Lead optimization
Kerns, E.; Di, L. Drug-like Properties: Concepts, Structure Design and Methods; Kerns, E., Ed.; Elsevier Inc., 2008.
- Manufacturing
- Side effects monitoring
- Formulation
- Phase 1: human safety
- Phase 2: human
efficiency
- Phase 3: large scale
efficiency
76. University of Helsinki
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Drug discovery
Drug discovery: the process by which new candidate medications are discovered
Long process ~20 years for a new drug
Only 55 drugs approved by the Food and Drug Administration in 2015
Costly process $51.2 billion invested in 2014 in Biopharmaceutical Research Industry
Mullard, A. 2015 FDA Drug Approvals. Nat. Publ. Gr. 2016, 15, 73–76
(PhRMA. Profile Biopharmaceutical Research Industry. Pharm. Res. Manuf. Am. 2015, 76..
77. University of Helsinki
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?
Predict the recognition
• Profile a ligand for a target (drug design)
• Prioritize a research approach
• Estimated side effects
• Toxicity
• …
However, the protein-ligand recognition is a complex process which includes
many factors difficult to model.
78. University of Helsinki
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Structure data, origins
In 1958 John Kendrew and Max Perutz published the first high-
resolution crystalized protein, sharing the Nobel prize in 1962.
It was the first time where protein structure (protein =
fundamental element for the biologic processes) was approached
with an atomic level.
Fersht, A.R. (2008). From the first protein structures to our current knowledge of protein folding: delights and scepticisms. Nat. Rev. Mol. Cell Biol. 9: 650–654.
Kendrew, J.C., Bodo, G., Dintzis, H.M., Parrish, R.G., Wyckoff, H., and Phillips, D.C. (1958). A three-dimensional model of the myoglobin molecule obtained by
x-ray analysis. Nature 181: 662–666.
Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, H., Will, G., and North, A.C. (1960). Structure of haemoglobin: a three-dimensional Fourier synthesis at
5.5-A. resolution, obtained by X-ray analysis. Nature 185: 416–422.
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Structure-based (and ligand-based) methods based on 3D structures.
Protein crystallized
X-rays are diffracted by each atoms
presented in the crystal structure
Structure data, today
80. University of Helsinki
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Importance
Estimates suggest that around 10-15% of
human genome may be druggable (with
small molecule approach) and 600-1500
potential targets
Druggability is important to:
- prioritize potential targets
- avert targets that are unlikely to bind
small molecules with high affinity
(optimize experimental screenings)
- Rational the target space
Human genome ~30,000
Druggable
Genome
~3,000
Diseases
modifying
Genes
~3,000
Drug targets ~ 600-1,500
81. University of Helsinki
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Dataset
Non redundant dataset: NRDLD (Non Redundant set of Druggable and Less
Druggable binding sites)
Adapted from Krasowski, A. et al. (2011). J.
Med. Chem Inf, 51(11), 2829–42
Experimental:
- HTS
- NMR screening
Database
screening
71 druggable binding sites
44 less druggable binding sites
Widely Characterized Apo protein set included in “Druggable
Cavity Directory” (139 proteins)
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2. Compute Linear Discriminant Analysis
(LDA) models with n descriptors
1. Define training and test set
by pocket estimation methods
Learning phase
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3. select best models with minimal number
of descriptors
Objective:
- parsimonious model
- Considering several pocket sets
Matthew's Coefficient Correlation
Consensus PockDrug
Learning phase
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PockDrug quality
Consensus PockDrug
prox-
test
DoGSite-
test
fpocket-
test
Acc 95 % 87 % 87 %
MCC 0.89 0.73 0.71
Robust on estimations
Good performances for different
pocket sets
fpocket-
score
DoGSite-
Scorer
Acc 76 % 76 %
MCC 0.51 0.54
Better that other models
fpocket-
apo
DoGSite-
apo
Acc 91 % 94 %
MCC 0.45 0.53
Apo pockets
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External validation
PockDrug model was validated on different pocket test sets and was compared of other
druggability models available in the literature
Robust performances on
different pocket test set.
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Properties
Combination of 4 pocket properties
Hydrophobicity
Geometry
Aromaticity
Atom type
Hydrophobicity ++++
Geometric +++
Atom type (H-bond donor-acceptor) ++
Aromaticity +
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To the ligand profiling
Which profile of ligand?
Druggable pocket
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Computational approaches are important to:
• To identify LSR
• Stock in databases
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In silico approaches
3 types of method to identify bioisosteres :
• Rational approaches, based on similar compounds (BIOSTER or SwissBioisostere)
• Literature searching (limited on precise case)
• Chemoinformatics based on a investigation of the chemical space or protein
complexes
Devereux, M., and Popelier, P.L. a (2010). In silico techniques for the identification of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10:
657–668.
Ujváry, I. (1997). BIOSTER-a database of structurally analogous compounds. Pestic. Sci. 51: 92–95.
Wirth, M., Zoete, V., Michielin, O., and Sauer, W.H.B. (2013). SwissBioisostere: A database of molecular replacements for ligand design. Nucleic Acids Res.
41: 1137–1143.
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Chemoinformatic approaches is based on investigation of chemical space or
X-ray complexes.
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Identification of LSR
Fingerprint interaction Similarity of binding sites using pharmacophores
Desaphy, J., and Rognan, D. (2014). Sc-PDB-Frag: A database of protein-ligand interaction patterns for bioisosteric replacements. J. Chem. Inf. Model. 54:
1908–1918.
Wood, D.J., Vlieg, J. De, Wagener, M., and Ritschel, T. (2012). Pharmacophore fingerprint-based approach to binding site subpocket similarity and its
application to bioisostere replacement. J. Chem. Inf. Model. 52: 2031–2043.
Interaction
Pharmacophores
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Molecular recognition
Many parameters influence the molecular recognition, such as molecular
interaction, flexibility, solvent exposition.
A same binding site can host different ligand,
modulating molecular interaction or binding site
flexibility.
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Mechanisms of replacements
Local substructures replace the metal
The nitrogen replaces the metal and interactions with the protein are conserved.
Phosphate Local replacement Nitrogen replace
the Mg2+
PDB code: 3ULI – 4EOM
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Molecular interactions
Hydrogen bonds Salt bridges π-πHalogen bonds
cation-π anion-π
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Publication IV
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Publication IV
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Polar contacts
• A sphere of 3.0 Å radius from
point charges carry the
majority of the information
about polar contacts.
• 80% of primary amine are
ionized.
Open some perspectives in interaction
modeling where a distance of 4 Å is
usually considered. Most frequent case
the amine is ionized.
Files, S., Sarthi, P., Gupta, S., Nayek, A., Banerjee, S., Seth, P., et al. (2015). SBION2 : Analyses of Salt Bridges from Multiple Structure Files, Version 2.
11: 2–5.
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Neighborhood analysis
Oxygen in carboxylate Oxygen in hydroxyl Oxygen in water molecules
Position of each atom type is discussed separately and considering the environment.
97. University of Helsinki
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Modeling the environment
2D projection, using a correspondence from the contingency table of all primary amine
considered in the dataset. Two type of interactions are considered include a Oxygen
carboxylate or not (‘)
Consider the fourth neighbors atoms in the both
environment
Distance between the
neighbor position and the
type of atom characterize
the dependence
1 +++ carboxylate
3 +++ hydroxyl
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Ionizable interactions
In the type of the very strong molecular interaction coupling electrostatic
interaction carried by the charges and a hydrogen-bond.
Focused on a type of strong protein-ligand interaction, well characterized in the
intra-protein interaction but poorly characterized in the protein-ligand interaction
• Protein stability
• Thermo-resistance
• Molecular mechanisms
(nucleation, enzyme process)
99. University of Helsinki
Université Paris Diderot 26-05-201699
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
Publication V
100. University of Helsinki
Université Paris Diderot 26-05-2016100
Division of pharmaceutical chemistry and technology – Faculty of pharmacy
Molécules Thérapeutiques in Silico – INSERM UMRS 973
LDA
Linear discriminant analysis (LDA) is a generalization of
Fisher's linear discriminant, a method used in statistics,
pattern recognition and machine learning to find a linear
combination of features that characterizes or separates
two or more classes of objects or events. The resulting
combination may be used as a linear classifier, or, more
commonly, for dimensionality reduction before later
classification.
Editor's Notes
Important concept to understand
Behind this sentence, we define the protein-ligand recognition
Related to the molecular recognition, how a ligand is recognize by a protein target
Using the expertise of the two laboratories
In relationship with the high
Similarly considering the proteins, any protein is not important for the drug discovery
separated
Minimal number of descriptors as it is possible
Increase the quality of previous model independently to the pocket estimations
2000 different visitors since January 2015
Considere a important concept in medicinal chemistry and in the drug optimization
We introduced some chemical modifications, which are generally tolerated by the target
Sequence of modifications
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Are superimposed each together
Just to formalize
First step is based on SMILES containing
Are superimposed each together
However, cogeners
This modeling is not enough in several cases
Prevalent environement and missing environement
Computational methods uselful for the drug discovery process and which
Using the expertise of the two laboratories
As the potential bioisosteric replacement, well classified the local replacements