The document discusses key concepts in pharmacokinetics and drug design including:
- Pharmacokinetics (PK) describes what the body does to a drug through absorption, distribution, metabolism and excretion. Pharmacodynamics (PD) describes what the drug does to the body.
- For a drug to be effective it must reach its target in sufficient concentrations, but PK variability can impact efficacy and toxicity.
- Computational tools like SwissADME can predict physicochemical properties, absorption, and "drug-likeness" to help optimize properties during drug design and avoid late-stage failures.
QSAR attempts to correlate biological activity to measurable physicochemical properties through mathematical equations. It relates parameters like lipophilicity (log P), electronic effects (Hammett constants), and steric effects to biological response. Various QSAR methods exist, including Hansch analysis which uses these substituent constants in its equations. More advanced techniques like CoMFA analyze fields around aligned molecules to model activity landscapes and identify favorable regions for activity. QSAR provides a framework for drug design and predicting activities of untested compounds.
Pharmacophore modeling identifies key molecular features necessary for drug-target binding and biological response. It represents molecules schematically in 2D or 3D. Pharmacophore features include hydrogen bond donors/acceptors, aromaticity, hydrophobicity and hydrophilicity. Pharmacophore models are used for virtual screening to identify molecules that may activate or inhibit a target. There are two main types: ligand-based models extract common features of known ligands, while structure-based models define features from protein-ligand complex structures. Both aim to encode the optimal 3D arrangement of interactions between ligands and targets.
This document discusses QSAR (quantitative structure-activity relationship), which is a mathematical model that relates biological activity to physicochemical properties of molecules. QSAR can be used to predict activity of new compounds. Key parameters used in QSAR include hydrophobicity (measured by partition coefficient), electronic effects (Hammett constant), and steric effects (Taft's steric factor). These parameters influence drug absorption, distribution, and interactions with receptors. QSAR models take the form of biological activity = function(parameters) to develop relationships between activity and molecular properties.
Pharmacophore modeling and docking techniqueskencha swathi
Pharmacophore modeling and docking techniques are important in rational drug design. Pharmacophore modeling identifies common molecular features necessary for biological activity. It represents features as 3D structures, 2D substituents, or properties. Pharmacophore modeling is used in structure-based drug design through target identification and ligand-based design via virtual screening. Docking predicts ligand binding orientation to form stable complexes. It identifies correct poses and binding affinities to aid hit identification and drug discovery. Popular docking software includes AutoDock, GOLD, Hex and SANJEEVINI.
In this slide I covered the detailed about hansch analysis, Free-Wilson analysis, and Mixed approach. I also gave a detailed application for each points.
This document discusses various applications of quantitative structure-activity relationships (QSAR) in drug research and development. It describes how QSAR can provide information on receptor sites from enzyme inhibition studies, help understand the importance of physicochemical properties like log P values, and enable the use of bioisosterism to modify compounds. QSAR has correctly predicted drug activity and toxicity and is useful in drug design before compounds are synthesized.
Combinatorial chemistry allows for the rapid synthesis of large libraries of compounds. It works by synthesizing many structures in parallel rather than one at a time. There are two main approaches: solid phase synthesis which attaches compounds to resin beads to isolate products, and solution phase which synthesizes in solvent. The libraries can be screened to identify active compounds more efficiently than traditional methods. This technique has increased the success of drug discovery by allowing testing of more structures at once.
QSAR attempts to correlate biological activity to measurable physicochemical properties through mathematical equations. It relates parameters like lipophilicity (log P), electronic effects (Hammett constants), and steric effects to biological response. Various QSAR methods exist, including Hansch analysis which uses these substituent constants in its equations. More advanced techniques like CoMFA analyze fields around aligned molecules to model activity landscapes and identify favorable regions for activity. QSAR provides a framework for drug design and predicting activities of untested compounds.
Pharmacophore modeling identifies key molecular features necessary for drug-target binding and biological response. It represents molecules schematically in 2D or 3D. Pharmacophore features include hydrogen bond donors/acceptors, aromaticity, hydrophobicity and hydrophilicity. Pharmacophore models are used for virtual screening to identify molecules that may activate or inhibit a target. There are two main types: ligand-based models extract common features of known ligands, while structure-based models define features from protein-ligand complex structures. Both aim to encode the optimal 3D arrangement of interactions between ligands and targets.
This document discusses QSAR (quantitative structure-activity relationship), which is a mathematical model that relates biological activity to physicochemical properties of molecules. QSAR can be used to predict activity of new compounds. Key parameters used in QSAR include hydrophobicity (measured by partition coefficient), electronic effects (Hammett constant), and steric effects (Taft's steric factor). These parameters influence drug absorption, distribution, and interactions with receptors. QSAR models take the form of biological activity = function(parameters) to develop relationships between activity and molecular properties.
Pharmacophore modeling and docking techniqueskencha swathi
Pharmacophore modeling and docking techniques are important in rational drug design. Pharmacophore modeling identifies common molecular features necessary for biological activity. It represents features as 3D structures, 2D substituents, or properties. Pharmacophore modeling is used in structure-based drug design through target identification and ligand-based design via virtual screening. Docking predicts ligand binding orientation to form stable complexes. It identifies correct poses and binding affinities to aid hit identification and drug discovery. Popular docking software includes AutoDock, GOLD, Hex and SANJEEVINI.
In this slide I covered the detailed about hansch analysis, Free-Wilson analysis, and Mixed approach. I also gave a detailed application for each points.
This document discusses various applications of quantitative structure-activity relationships (QSAR) in drug research and development. It describes how QSAR can provide information on receptor sites from enzyme inhibition studies, help understand the importance of physicochemical properties like log P values, and enable the use of bioisosterism to modify compounds. QSAR has correctly predicted drug activity and toxicity and is useful in drug design before compounds are synthesized.
Combinatorial chemistry allows for the rapid synthesis of large libraries of compounds. It works by synthesizing many structures in parallel rather than one at a time. There are two main approaches: solid phase synthesis which attaches compounds to resin beads to isolate products, and solution phase which synthesizes in solvent. The libraries can be screened to identify active compounds more efficiently than traditional methods. This technique has increased the success of drug discovery by allowing testing of more structures at once.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
QSAR (Quantitative Structure Activity Relationship) is a computational method that correlates chemical structures or properties of compounds to their biological activity. It transforms chemical structures into numerical descriptors for properties like solubility or log P (partition coefficient). QSAR models then use these descriptors in mathematical formulas to quantitatively predict biological activity based on structural or physicochemical properties. Key parameters used in QSAR include log P (measure of lipophilicity), Hammett constants (measure electron donating/withdrawing ability), and Taft steric parameters (measure steric effects on size and shape). Hansch analysis and linear free energy models are commonly used methods to perform regression analysis of biological activity data and generate predictive QSAR models.
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
Combinatorial chemistry is a technique used to rapidly produce large libraries of potential drug molecules. It allows scientists to create and evaluate thousands of similar compounds in parallel. The key advantages are that it is faster and more economical than traditional drug discovery methods. Some challenges include ensuring diversity in the compound libraries and identifying the active components within mixture samples. Solid phase synthesis and parallel/mixed synthesis are common techniques used in combinatorial chemistry approaches.
This document provides an introduction to quantitative structure-activity relationships (QSAR). It defines QSAR as quantifying physicochemical properties of drugs to see their effect on biological activity. Graphs are used to plot activity versus properties, and regression analysis determines correlation. Key properties discussed are hydrophobicity, steric effects, and electronic effects. Hansch analysis uses equations to relate activity to multiple properties. Advantages are understanding structure-activity and enabling novel analog design. Limitations include potential for false correlations and need for large, high-quality datasets.
The document discusses pharmacophores, which are abstract descriptions of molecular features necessary for molecular recognition between a ligand and biological macromolecule. A pharmacophore consists of 3D structural features like hydrophobic groups and hydrogen bond donors/acceptors. Pharmacophore mapping is used to define pharmacophoric features and align molecules to identify common binding elements responsible for biological activity. Pharmacophore models can be used in virtual screening to filter large databases and identify new compounds that may bind similarly to known active molecules. The document provides details on different approaches for pharmacophore generation and searching compound libraries.
This presentation discusses drug target identification and validation. It introduces drug targets as specific sites where drugs bind to exert their therapeutic effects. Target identification methods include genomics, proteomics, and bioinformatics. Targets are then validated using techniques like siRNAs and antisense oligonucleotides to demonstrate the functional role of targets in disease and ensure drug interactions produce the desired therapeutic response.
Molecular docking is a method for predicting how two molecules, such as a ligand and its protein target, will interact and fit together in three dimensions. Docking has become an important tool in drug discovery for identifying potential binding conformations between drug candidates and protein targets. The key steps in a typical docking workflow involve selecting the receptor and ligand molecules, then using software to computationally predict the orientation of binding and evaluate the fit through scoring functions. Popular molecular docking software packages include AutoDock, GOLD, and Glide. Applications of docking include virtual screening in drug discovery and lead optimization.
The document discusses prodrug design and its applications. Prodrugs are biologically inert derivatives of drug molecules that undergo conversion in vivo to release the active parent drug. The objectives of prodrug design are to improve pharmaceutical and pharmacokinetic properties like solubility, stability, absorption and bioavailability. Prodrugs can be classified based on the carrier group attached and site of bioactivation. Applications include masking taste/odor, reducing irritation, enhancing solubility, stability and bioavailability to improve drug delivery. In summary, prodrug design is a strategy to overcome undesirable drug properties and improve therapeutic effectiveness.
Presentation on insilico drug design and virtual screeningJoon Jyoti Sahariah
This presentation discusses in silico drug design and virtual screening techniques. It defines in silico drug design as using computational software to perform drug design based on knowledge of a biological target. The presentation outlines two main types of in silico drug design: ligand-based, which uses known ligands to derive a pharmacophore when the receptor is unknown, and structure-based, which relies on knowledge of the 3D receptor structure. It also describes virtual screening techniques as automatically evaluating large compound libraries for likelihood of binding to a target protein using computer programs based on ligand or structure models.
This document provides an overview of prodrug design and practical considerations. It defines prodrugs as chemically inert precursors that release the active pharmacological compound. Prodrugs are classified based on their carrier and linker groups. The rationale for prodrug design includes improving solubility, enhancing membrane permeability, reducing pre-systemic metabolism, and targeting delivery to specific sites. Practical considerations for prodrug design involve the use of ester, amide, phosphate and carbamate groups to link the drug. The document discusses several examples of prodrugs and their advantages over parent drugs.
The document provides an overview of the drug discovery and development process. It discusses the various stages involved, including target selection using genomics, proteomics and bioinformatics; lead discovery through synthesis, isolation and high-throughput screening; medicinal chemistry such as structure-activity relationships studies; in vitro and preclinical in vivo testing in animal models; and clinical trials in humans. The timeline for this process can span over 10-15 years from drug target identification to regulatory approval. Key techniques and approaches at each stage are also summarized.
Pharmacophore modelling and docking techniques.pptxNIDHI GUPTA
Pharmacophore modelling is used to identify the essential molecular features necessary for a ligand to bind to a biological target. A pharmacophore model can explain how structurally diverse ligands can bind to a common receptor site and can be used to design or discover new ligands that bind to the same receptor. Docking techniques are also used to model molecular recognition between ligands and biological targets. Together, pharmacophore modelling and docking can provide insights into ligand-receptor interactions and guide drug design and discovery efforts.
Fragment-based drug design (FBDD) uses small molecular fragments that bind weakly to a target protein's binding site. These fragments can then be grown, merged, or linked to improve binding affinity. FBDD provides starting points for challenging targets like protein-protein interactions. It increases the use of biophysics to characterize compound binding. FBDD also gives small research groups access to tools for identifying chemical probes of biological systems.
Mass spectrometry deals with studying charged molecules and fragment ions produced from a sample exposed to ionizing conditions. It provides the relative intensity spectrum based on ions' mass to charge ratio, allowing identification of unknown compounds. The document discusses the basic principles, advantages, disadvantages, instrumentation, applications, and analysis techniques of mass spectrometry.
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
This document summarizes information about several antifungal drugs, including their molecular formulas, properties, synthesis methods, and uses. It discusses Ketoconazole, Terconazole, Metronidazole, and Miconazole. For each drug, it provides the molecular formula, describes the synthesis starting from various reactants and reaction steps, and lists clinical uses such as treating fungal infections, jock itch, and vaginal thrush. The document aims to provide information on the preparation and applications of important antifungal heterocyclic compounds.
Thermal analysis techniques monitor a property of a sample against temperature while the sample is heated in a controlled atmosphere. There are several types of thermal analysis including differential scanning calorimetry (DSC), differential thermal analysis (DTA), and thermal gravimetric analysis (TGA). DSC measures the heat flow into or out of a sample during heating or cooling. DTA monitors the temperature difference between a sample and inert reference as heat is applied. These techniques are useful for characterizing pharmaceutical samples and studying thermal transitions such as glass transitions, crystallization, and melting.
The document discusses various considerations for identifying central nervous system (CNS) drugs, including bioavailability. It defines bioavailability as the amount of drug available in the body to act at the target. Three key points are made: 1) Drugs must reach the CNS target area in sufficient amounts during the appropriate time window to have efficacy, otherwise bioavailability limits efficacy; 2) Molecular properties influence absorption, distribution, metabolism and excretion of drugs, impacting bioavailability; 3) Case studies show how changes in CNS bioavailability and metabolism can impact drug safety and efficacy.
Analytical Method Development and Validation of Prednisolone Sodium Phosphate...iosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
QSAR (Quantitative Structure Activity Relationship) is a computational method that correlates chemical structures or properties of compounds to their biological activity. It transforms chemical structures into numerical descriptors for properties like solubility or log P (partition coefficient). QSAR models then use these descriptors in mathematical formulas to quantitatively predict biological activity based on structural or physicochemical properties. Key parameters used in QSAR include log P (measure of lipophilicity), Hammett constants (measure electron donating/withdrawing ability), and Taft steric parameters (measure steric effects on size and shape). Hansch analysis and linear free energy models are commonly used methods to perform regression analysis of biological activity data and generate predictive QSAR models.
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
Combinatorial chemistry is a technique used to rapidly produce large libraries of potential drug molecules. It allows scientists to create and evaluate thousands of similar compounds in parallel. The key advantages are that it is faster and more economical than traditional drug discovery methods. Some challenges include ensuring diversity in the compound libraries and identifying the active components within mixture samples. Solid phase synthesis and parallel/mixed synthesis are common techniques used in combinatorial chemistry approaches.
This document provides an introduction to quantitative structure-activity relationships (QSAR). It defines QSAR as quantifying physicochemical properties of drugs to see their effect on biological activity. Graphs are used to plot activity versus properties, and regression analysis determines correlation. Key properties discussed are hydrophobicity, steric effects, and electronic effects. Hansch analysis uses equations to relate activity to multiple properties. Advantages are understanding structure-activity and enabling novel analog design. Limitations include potential for false correlations and need for large, high-quality datasets.
The document discusses pharmacophores, which are abstract descriptions of molecular features necessary for molecular recognition between a ligand and biological macromolecule. A pharmacophore consists of 3D structural features like hydrophobic groups and hydrogen bond donors/acceptors. Pharmacophore mapping is used to define pharmacophoric features and align molecules to identify common binding elements responsible for biological activity. Pharmacophore models can be used in virtual screening to filter large databases and identify new compounds that may bind similarly to known active molecules. The document provides details on different approaches for pharmacophore generation and searching compound libraries.
This presentation discusses drug target identification and validation. It introduces drug targets as specific sites where drugs bind to exert their therapeutic effects. Target identification methods include genomics, proteomics, and bioinformatics. Targets are then validated using techniques like siRNAs and antisense oligonucleotides to demonstrate the functional role of targets in disease and ensure drug interactions produce the desired therapeutic response.
Molecular docking is a method for predicting how two molecules, such as a ligand and its protein target, will interact and fit together in three dimensions. Docking has become an important tool in drug discovery for identifying potential binding conformations between drug candidates and protein targets. The key steps in a typical docking workflow involve selecting the receptor and ligand molecules, then using software to computationally predict the orientation of binding and evaluate the fit through scoring functions. Popular molecular docking software packages include AutoDock, GOLD, and Glide. Applications of docking include virtual screening in drug discovery and lead optimization.
The document discusses prodrug design and its applications. Prodrugs are biologically inert derivatives of drug molecules that undergo conversion in vivo to release the active parent drug. The objectives of prodrug design are to improve pharmaceutical and pharmacokinetic properties like solubility, stability, absorption and bioavailability. Prodrugs can be classified based on the carrier group attached and site of bioactivation. Applications include masking taste/odor, reducing irritation, enhancing solubility, stability and bioavailability to improve drug delivery. In summary, prodrug design is a strategy to overcome undesirable drug properties and improve therapeutic effectiveness.
Presentation on insilico drug design and virtual screeningJoon Jyoti Sahariah
This presentation discusses in silico drug design and virtual screening techniques. It defines in silico drug design as using computational software to perform drug design based on knowledge of a biological target. The presentation outlines two main types of in silico drug design: ligand-based, which uses known ligands to derive a pharmacophore when the receptor is unknown, and structure-based, which relies on knowledge of the 3D receptor structure. It also describes virtual screening techniques as automatically evaluating large compound libraries for likelihood of binding to a target protein using computer programs based on ligand or structure models.
This document provides an overview of prodrug design and practical considerations. It defines prodrugs as chemically inert precursors that release the active pharmacological compound. Prodrugs are classified based on their carrier and linker groups. The rationale for prodrug design includes improving solubility, enhancing membrane permeability, reducing pre-systemic metabolism, and targeting delivery to specific sites. Practical considerations for prodrug design involve the use of ester, amide, phosphate and carbamate groups to link the drug. The document discusses several examples of prodrugs and their advantages over parent drugs.
The document provides an overview of the drug discovery and development process. It discusses the various stages involved, including target selection using genomics, proteomics and bioinformatics; lead discovery through synthesis, isolation and high-throughput screening; medicinal chemistry such as structure-activity relationships studies; in vitro and preclinical in vivo testing in animal models; and clinical trials in humans. The timeline for this process can span over 10-15 years from drug target identification to regulatory approval. Key techniques and approaches at each stage are also summarized.
Pharmacophore modelling and docking techniques.pptxNIDHI GUPTA
Pharmacophore modelling is used to identify the essential molecular features necessary for a ligand to bind to a biological target. A pharmacophore model can explain how structurally diverse ligands can bind to a common receptor site and can be used to design or discover new ligands that bind to the same receptor. Docking techniques are also used to model molecular recognition between ligands and biological targets. Together, pharmacophore modelling and docking can provide insights into ligand-receptor interactions and guide drug design and discovery efforts.
Fragment-based drug design (FBDD) uses small molecular fragments that bind weakly to a target protein's binding site. These fragments can then be grown, merged, or linked to improve binding affinity. FBDD provides starting points for challenging targets like protein-protein interactions. It increases the use of biophysics to characterize compound binding. FBDD also gives small research groups access to tools for identifying chemical probes of biological systems.
Mass spectrometry deals with studying charged molecules and fragment ions produced from a sample exposed to ionizing conditions. It provides the relative intensity spectrum based on ions' mass to charge ratio, allowing identification of unknown compounds. The document discusses the basic principles, advantages, disadvantages, instrumentation, applications, and analysis techniques of mass spectrometry.
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
This document summarizes information about several antifungal drugs, including their molecular formulas, properties, synthesis methods, and uses. It discusses Ketoconazole, Terconazole, Metronidazole, and Miconazole. For each drug, it provides the molecular formula, describes the synthesis starting from various reactants and reaction steps, and lists clinical uses such as treating fungal infections, jock itch, and vaginal thrush. The document aims to provide information on the preparation and applications of important antifungal heterocyclic compounds.
Thermal analysis techniques monitor a property of a sample against temperature while the sample is heated in a controlled atmosphere. There are several types of thermal analysis including differential scanning calorimetry (DSC), differential thermal analysis (DTA), and thermal gravimetric analysis (TGA). DSC measures the heat flow into or out of a sample during heating or cooling. DTA monitors the temperature difference between a sample and inert reference as heat is applied. These techniques are useful for characterizing pharmaceutical samples and studying thermal transitions such as glass transitions, crystallization, and melting.
The document discusses various considerations for identifying central nervous system (CNS) drugs, including bioavailability. It defines bioavailability as the amount of drug available in the body to act at the target. Three key points are made: 1) Drugs must reach the CNS target area in sufficient amounts during the appropriate time window to have efficacy, otherwise bioavailability limits efficacy; 2) Molecular properties influence absorption, distribution, metabolism and excretion of drugs, impacting bioavailability; 3) Case studies show how changes in CNS bioavailability and metabolism can impact drug safety and efficacy.
Analytical Method Development and Validation of Prednisolone Sodium Phosphate...iosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Dr. Praveen Balimane, senior staff fellow, Division of Clinical Pharmacology-1 at OCP/OTS/CDER/FDA, spoke during the Society for Laboratory Automation and Screening ADMET Special Interest Group Meeting on “Transporter Evaluation in Drug Development.”
Transporters, like CYPs, are being recognized as proteins that can play a pivotal role in dictating the ADME properties of drugs. A thorough understanding of potential roles of transporters in drug interactions and toxicity is important in drug development. The talk provided a high level overview of various transporter evaluation initiatives at the agency. Some of the topics discussed:
• On-going efforts on decision trees within the DDI guidance
• Novel emerging transporters impacting ADME
• Inter-play of hepatic transporters and liver-toxicity
• Inter-play of renal transporters and renal function
Bioanalytical support plays a vital role during the lead optimization stages. The major goal of the bioanalysis is to assess the over-all ADME characteristics of the NCEs and biologics. Bioanalytical tools can play a significant role and impact the progress in drug discovery and development. Dramatic increases in investments in new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and ADC, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME and PK properties.https://www.medicilon.com/blog/featured-stories/dmpk-bioanalysis/
The document discusses computational models that have been and can be used for predicting human toxicities. It provides examples of models that have been developed for predicting various physicochemical properties, interactions with proteins, and toxicity outcomes like mutagenicity, environmental toxicity, and drug-induced liver injury. It also outlines future areas that could be modeled, like mixtures and more specific protein targets. The key enablers of these models are increased computing power and data availability from literature and open sources.
This document discusses various topics related to drug discovery through bioinformatics and computational approaches. It covers target identification and validation, high-throughput screening, developing hits into leads, evaluating drug-likeness of compounds using rules like Lipinski's Rule of Five, and using computational descriptors for virtual screening. The goal is to discuss how computational tools can help streamline the drug discovery process by aiding in target selection and validation, compound screening and optimization of leads.
1.preformulation concept in Modern pharmaceutics.pptxPNMallikarjun
Preformulation is defined as the investigation of physical and chemical properties of a drug substance alone and when combined with excipients. The goal is to generate information to help formulators develop stable and safe dosage forms with good bioavailability. Some key tests include determining the drug's solubility, stability, and compatibility with various excipients using techniques like DSC, TLC, and HPLC. This provides critical data to guide the rational selection of dosage form and formulation components.
The document discusses various topics related to drug discovery including target identification and validation, high-throughput screening, hit and lead identification, computational approaches like docking and de novo design, and clinical trial phases. It provides definitions for key terms like target, screening, hit, and lead. It also discusses sources for screening libraries and describes factors to consider for an optimal drug target.
The document discusses various topics related to drug discovery through bioinformatics and computational approaches. It begins by discussing comparative genomics and using knowledge about model organisms to identify similar biological areas and pathways in other species. It also discusses topics like high-throughput screening of large libraries, the definitions of targets, hits and leads in drug discovery, and approaches like using RNAi and phenotypic screening in model organisms. Finally, it discusses computational methods that can be used throughout the drug discovery process, including for target identification and validation, virtual screening, assessing drug-likeness of compounds, and describing compounds using structural and physicochemical descriptors.
The Butterfly Effect: How to see the impact of small changes to your ADCMilliporeSigma
This document summarizes key aspects of characterizing antibody drug conjugates (ADCs), including:
1) A case study examining how different polyethylene glycol (PEG) linker sizes affect ADC structure and target binding. Peptide mapping by mass spectrometry showed conjugation sites varied with linker size. Hydrogen/deuterium exchange mass spectrometry showed conjugation induced conformational changes.
2) Methods for assessing ADC mechanisms of action, including measuring internalization, cytotoxicity, and effector functions.
3) An overview of MilliporeSigma's comprehensive ADC product characterization and biosafety testing services across multiple sites.
The Butterfly Effect: How to see the impact of small changes to your ADCMerck Life Sciences
Watch this webinar here: https://bit.ly/31PRr2z
Small changes to the design of antibody-drug conjugate can have a dramatic effect on its structure and biological activity. Effective product characterization is essential to understanding the impact of these changes. Here we discuss methods to provide insight at critical junctures in ADC development.
There are many different design considerations facing developers of antibody-drug conjugates: these variables must be tuned to achieve the right balance of efficacy and safety. For example, the choice of linker can influence an ADC's potency, toxicity and pharmacokinetics.
In this webinar we explore the influence of various PEG linkers on the structure of a model ADC by identifying specific sites of conjugation by peptide mapping, investigating changes in higher order structure by HDX mass spectrometry, and examining the impact on binding by SPR spectroscopy.
We demonstrate that employing a range of orthogonal methods is critical to understanding the structure-function relationships of an ADC.
In this webinar, you will learn about:
• How the choice of linker can influence an ADC's activity
• Information-rich methods to probe ADC structure and function
• Effective strategies for thorough characterization of ADC products
This document discusses bioassays and techniques for measuring drug-target binding in drug development. It provides an overview of the multi-step drug development process and defines bioassays as assays involving biological samples that can be quantal or graded. It then describes various methods for measuring drug-target binding affinity, including radioligand binding assays, isothermal titration calorimetry, surface plasmon resonance, and microscale thermophoresis. These binding assays are important for both hit identification and lead optimization in drug development.
This document summarizes research aimed at developing new inhibitors of the glycogen phosphorylase (GP) enzyme for potential treatment of type 2 diabetes. Computational models were developed to predict the activity of 21 test ligands as GP inhibitors. The models suggested ligands S1, S2, and S21 may be potent nanomolar inhibitors of GP, among the best known. These promising ligands will now be synthesized and experimentally tested for their ability to inhibit GP.
MOLECULAR DOCKING AND RELATED DRUG DESIGN ACHIEVEMENTS santosh Kumbhar
Molecular docking is a computational method used in structure-based drug design to predict how biological macromolecules interact with other molecules. It attempts to predict the preferred orientation of one molecule to another when bound to each other to form a stable complex. Docking is useful for predicting the binding orientation of small molecule drug candidates to their protein targets in order to predict their interaction and to design effective inhibitors. There are various types of docking software available that implement different algorithms to predict the binding orientation and affinity between molecules rapidly and accurately to help identify potential lead compounds for drug development. Molecular docking has contributed to the discovery of several new drug classes and is playing an increasingly important role in modern computer-aided drug design and virtual
The document provides an overview of preformulation studies, which are conducted to determine the physicochemical properties of new drug substances prior to formulation development. Key aspects covered include definition of preformulation, factors considered, objectives, outcomes, common tests like solubility and stability studies, and techniques involved. Specifically, the document discusses solubility analysis methods, factors affecting solubility like pH and temperature, and importance of solubility determination. It also covers common degradation pathways like oxidation and hydrolysis, and approaches to prevent or minimize degradation.
Access to Research
Date 11-08-2018
Venue Conference HAll NIAS IISc campus
Conference and workshops for clinical practitioners to introduce them to modern tools and an alternative approach to modern scientific research.
Purpose
1. Build a network of physicians across the country
2 Train physicians to analyse clinical data and restructure it to make it compatible with research standards
3. Introduce modern tools to understand the mechanism of actions of medicine
4. Introduce artificial intelligence and machine learning to clinical practitioners to support decision-making processes
Access to Science
Clinical experience and traditional knowledge are important sources of data that affect decision making processes in modern healthcare systems. This data should be made accessible for scientific evaluation and validation to improve healthcare worldwide. The Open Source Pharma Foundation believes that clinical practitioners from various disciplines should have the right to access research so that they can help identify problems, contribute their scientific knowledge, and support the discovery ecosystem.
Background
The majority of medical practitioners working on the ground level with patients do not take part in open clinical research worldwide. However, the data collected and owned by them plays an important role in establishing better discovery pathways. Through this workshop, we seek to open opportunities to enhance health care systems around the world and to overcome the following challenges faced by medical practitioners.
1. Regulatory limitations
2. Academic limitations
3. Time constraints
4. Lack of access to modern tools
Monoclonal antibodies: functional improvements at SPC shelf life limitsPharmaxo
Monoclonal antibody (mAb) therapeutics are one of the fastest growing sectors in the pharmaceutical industry and have already established themselves as frontline therapies in oncology.
Perspective on QSAR modeling of transportSean Ekins
The document discusses quantitative structure-activity relationship (QSAR) modeling of drug transporters. It summarizes the evolution of QSAR models for various transporters like P-glycoprotein (P-gp) and describes approaches like pharmacophore modeling that have been used to predict transporter substrates and inhibitors. Key challenges addressed are small datasets and evaluating model predictivity. The development and validation of Bayesian classification and quantitative pharmacophore models for transporters like the apical sodium-dependent bile acid transporter (ASBT) and organic cation transporter 2 (OCTN2) are also summarized.
Prediction of efficacy of repurposed antiviral drugs against COVID-19Ahmed Ali
This document discusses using a PK/PD approach to predict the efficacy of repurposed antiviral drugs for COVID-19. The objectives are to integrate PK and PD data to predict efficacy in clinical settings and validate hypotheses. The document analyzes several drugs including lopinavir/ritonavir, remdesivir, favipiravir and hydroxychloroquine. For each drug, it summarizes PK parameters, in vitro efficacy data, and clinical trial results. It predicts that most drugs like lopinavir/ritonavir are unlikely to achieve therapeutic levels in the lungs due to high protein binding and short half-lives. The document concludes that a PK/PD approach is an effective tool to predict efficacy
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Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
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TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
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Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
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Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
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Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
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TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
TEST BANK For Basic and Clinical Pharmacology, 14th Edition by Bertram G. Katzung, Verified Chapters 1 - 66, Complete Newest Version.
2. Pharmacodynamics
steps
Site
of
ac*on
Therapeu*c
(or
toxic)
effect
Interac*on
with
the
therapeu*c
target
(protein)
A Drug has to reach its therapeutic target
Pharmacokine2c
steps
(ADME)
Excression
Distribu*on
Absorp*on
Ac*ve
ingredient
(AI)
in
solu2on
Route
of
administra*on
Oral
Parenteral
(i.v.)
Libera*on
Pharmaceu2c
steps
Metabolism
Bioavailability (F): % AI reaching the systemic circulation unchanged. 2
3. • Pharmacodynamics (PD)
– describes and quantifes the response of the organism when taking the
drug,
– at clinical, biochemical or molecular level.
• The pharmacodynamic effect: « what the drug does to the body »
Drug
Body
Tissue
Organ
Receptor
• Activity
• Toxicity
pharmacocynamics
(PD)
Pharmacodynamics
3
4. • Pharmacokinetics (PK)
– describes and quantifies the changes on the drug made by the body,
– includes moves and chemical transformation of the molecule.
• The pharmacokinetic effect: « what the body does to the drug »
Drug
Body
Tissu
Organ
Receptor
Pharmacokine2cs
(PK)
• Absorption
• Distribution
• Metabolism
• Excretion
Pharmacokinetics
4
5. Interdependance of PD and PK
• The pharmacodynamic and pharmacokinetic have mutual influence
– Absorption, distribution et excretion (PK) impact the potency and duration of a drug (PD).
– Metabolism (PK) transforms drug molecules in substances (metabolites), which can
have a therapeutic or toxic effect (PD).
– A drug can affect a function in the organism (PD) and so its capacity to handle drug
molecule (PK).
• Activity
• Toxicity
Drug
Body
Pharmacodynamics
(PD)
Pharmacokine2cs
(PK)
• Absorption
• Distribution
• Metabolism
• Excretion
• Clinical development to
define dose and therapeutic
schemes:
– maximal curative effects
– minimal unwanted effects.
• Design drug candidates:
– potent
– secure (non-toxic)
– bioavaible (~oral)
5
6. Clinical impact of ADME
In vitro:
dose à concentration à effect
In vivo:
dose à ADME à concentration à effect
dose
(~
concentra*on)
effect
effect
dose
(~
concentra*on)
/
• To be effective a drug must be in sufficient concentration at the target
6
7. • Variability in ADME (mainly metabolism) can have a dramatic
impact on the efficacy and the toxicity of a drug.
• Schematic example, oral administration:
Time
Blood
c
oncentra*on
Threshold
for
efficacy
Threshold
for
toxicity
Toxicity
Inefficacy
Therapeu2c
Window
Clinical effect: efficacy vs toxicity
7
9. Estimation of ADME & PK: a need for Drug Discovery
Hugo Kubinyi Lectures: http://www.kubinyi.de
Preclinical Clinical
• PK in clinical failure tends to decrease because ADME accounted for at discovery phase.
• Promote molecules with good ADME properties and not necessarily most potent.
• The later in the drug development a compound is withdrawn, the more costly.
• At a very late stage, it may kill a department/site/company.
è point out ADME/tox issues as soon as possible (hit-to-lead / optimization).
è eliminate potentially problematic molecules at early stage.
9
10. Predictive ADME
• To estimate ADME behaviors and pharmacokinetic parameters for drug
design, computational models should be:
– robust à reliable prediction
– fast à numerous structures to be handled (also some « virtual » molecules).
– Descriptive enough and easy to interpret in chemical terms à main
objective: modify molecule to target specific properties.
• SwissADME web-based tools: access to multiple models for prediction of :
– Physicochemistry
(because ADME is directly related to the properties of the molecule)
– Some pharmacokinetic behaviors
(also graphical output)
– Druglikeness
– ... and others.
10
11. SwissAMDE: Output
One-panel-per-molecule
for clear output and export
Map of physchem properties
for qualitative PK interpretation
(e.g. Egan Egg1).
1Egan, W.J., Merz, K.M. & Baldwin, J.J., 2000. J. Med. Chem, 43(21), pp.3867–3877.
11
12. SwissADME: Physicochemical description of the
molecule
• Molecular description
• Simple counts of atoms/bonds
• Polar Surface Area (PSA, measure of
apparent polarity)
O NH
OH
49 Å2
Paracetamol
* Ertl, P., Rohde, B. & Selzer, P., 2000. Journal of medicinal chemistry, 43(20), pp.3714–3717. 12
Topological PSA (TPSA, fast 2D calculation).
ADME Guideline: TPSA < 140 Å2 good intestinal absorption.
TPSA < 70 Å2 good brain penetration.
13. SwissADME: Lipophilicity
• Important physicochemical properties quantified by
the partition coefficient log P between water and
n-octanol (immiscible)
logPo/w = log
Co
Cw
• Best solvent system to mimic recognition of molecules with:
– phospholipic membranes è ADME, pharmacokinetics,
– macromolecules (proteins) è Protein binding, pharmacodynamics.
13
Lipophilic molecules:
• well absorbed (A)
• access the brain (D)
• not released from albumin (D, blood)
• rapidly metabolized (M)
• less excreted by kidney (E).
hydrophilic molecules:
• not well absorbed (but suitable for injection)
• well distributed (except in the brain)
• metabolically stable but rapidly excreted.
lipophilicity
log P = 0
Optimum of lipophilicity for an oral drug (druglikeness)
14. SwissADME: Prediction of Lipophilicity
* Daina, A., Michielin, O. & Zoete, V., 2014 J. Chem. inf. model. 54(12), pp.3284–3301.
• Classes of log P methods:
– Fragmental (ex. SLOGP, XLOGP)
– Topological (ex. MLOGP)
– 3D Physics-based (iLOGP*)
• Multiple predictions by methods as different as possible
• Every method has strongpoint and weakness
è Possibility of consensus prediction (by now: simple average).
14
Σ = iLOGP= 2.21
Lipophilicity atomic contributions
lipophilic
hydrophilic
ΔGsolv
oct
= ΔGelec,solv
oct
+ ΔGnonpolar,solv
oct
ΔGsolv
wat
= ΔGelec,solv
wat
+ ΔGnonpolar,solv
wat
Solva2on
free
energy
in
octanol
Solva2on
free
energy
in
water
difference
~
iLOGP
15. SwissADME: Water solubility
• Governs absorption and impacts many processes in formulation.
• Historical model (GSE, general solubility equation)1:
log S = 0.5 − 0.01 × (m.p. °C − 25) − log P
49 Å2
1 Ran, Y. et al., 2001. J. Chem. inf. model., 41(5), pp.1208-1217
2 Ali, J. et al., 2012. J. Chem. inf. model., 52(2), pp.420–428.
• Ali2: replacement of melting point (difficult to predict) by TPSA:
log S = − 1.0377 × log P − 0.0210 × TPSA + 0.4488
• SwissADME: 3 multiple linear models linking log S with physicochemical
descriptors. è consensus approach.
• Quantitative classes, log S scale:
insoluble < -10 < poorly < -6 < moderately < -4 < soluble < -2 < very < 0 < highly
N=1256
r2=0.816
RMSE=0.719
15
16. SwissADME: Egan Egg for oral absorption1
• Simple, efficient and easily interpretable graphical model for oral absorption.
• Example: Mapping anti-cancer kinase inhibitors on the Egan Egg.
1 Egan, WJ et al. (2002) Adv Drug Deliver Rev 54:273-28
2 http://www.drugbank.ca
SLOGP
TPSA
99% probability well
absorbed by GI
(≥90% absorption)
95% probability well
absorbed by GI
(≥90% absorption)
DrugBank2: «Absorption
following oral administration of
lapatinib is incomplete and
variable. »
S
O
O
HN
O
N
N
NH
O
F
Cl
DrugBank2: «Maximum
plasma concentrations
(Cmax) of sunitinib are
generally observed
between 6 and 12 hours
(Tmax) following oral
administration.»
N
HN O
HN
O
HN
F
16
17. SwissADME: Egan Egg for oral absorption
1Huang WS et al. (2009) J Med Chem 52:4743–4756
2Huang WS et al. (2010) J Med Chem 53:4701–4719
SLOGP
TPSA
1
2
3
1 2 3
Correct
absorption
issue
1.6 nM Ponatinib 8.6 nM
9 nM
17
• Example: Lead optimization of Bcr-
Abl tyrosine kinase inhibitor leads to
anti-cancer Ponatinib1,2.
• PD and PK optimization steps.
è Can be an exercise for student. How
to optimize absorption starting from a
given molecule.
18. SwissADME: Druglikeness
• Lipinski Rule-of-five (Pfizer Ro5)1:
(not more than 1 criteria failing)
– MW < 500
– CLOGP < 5 (MLOGP < 4.15)2
– # H-bond donors ≤ 5
– # H-bond donors ≤ 10
• Egan filter (Pharmacopia)3:
– SLOGP < 6
– TPSA < 132 Å2
1Lipinski, C., et al., Adv. Drug Deliv. Rev., 1997, 23, 3.
2Lipinski, C., et al. Adv. Drug Delivery Rev. 2001, 46, 3.
3Egan, W.J., Merz, K.M., Baldwin, J.J., J. Med. Chem., 2000, 43, 3867.
• Druglikeness Rules:
Qualitative estimation defined by
ranges of specific physichochemical
properties that make a molecule a
possible oral drug.
• « Like a drug » from the PK (ADME)
point of view (bioavailable, not
necessarily bioactive).
• Druglike Filters:
Ranges based on computed/
predicted properties derived from
known oral drugs.
• Main use:
Filtering large chemical librairies.
• SwissADME:
Multiple filters for consensus
18
19. SwissADME: Medicinal Chemistry
1Baell, J.B. & Holloway, G.A., 2010. J. Med. Chem., 53(7), pp.2719–2740.
2Brenk, R. et al., 2008..ChemMedChem, 3(3), pp.435–444
3Ertl, P. & Schauffenhauer, A., 2009. J Cheminf., 1(1), p.8.
• ALERTS: Recognition of problematic fragments2, known to be:
– toxic, unstable, reactive
– aggregator è false positive in biological screening
– dye è perturbation of assays
• Synthetic accessibility score3, estimation of the ease of chemical
synthesis, combination of:
– Similarity with actually existing molecules in vendor catalogs
(list of molecules immediately available).
– Objective estimation of the complexity (macrocycles, chiral
centers, ...)
19
Is it worth to convince a chemist to synthesize the molecule ?
20. Synthetic accessibility score (SAscore)
• Estimation of the ease of synthesis, based on:
– Similarity: Cut the molecule into fragments (fingerprints) and compare to:
§ Fingerprints on “all-now” subset of ZINC (12,782,590 molecules).
§ 412,579 fragments were retrieved and ranked.
§ Frequent fragments è positive contributions.
§ Rare fragments è negative contributions.
– Complexity penalties: Count of specific chemical moieties.
§ Fused rings: / spiros: / macrocycles:
§ Stereocenter:
§ Number of atoms
• Finally normalized, so:
1 (easy) ≤ SAscore ≤ 10 (difficult)
20
21. SAscore: performance
21
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
SAscore
ChemistScore
• Comparison with estimation given by 9 chemists (ChemistScore)
for 40 molecules1.
• Good linear correlation
(R2=0.88)
• Good ranking correlation
(Spearman’s r=0.94).
1Ertl, P. & Schuffenhauer, A., 2009. J Cheminf., 1(1), p.8.
22. « All models are wrong
but some are useful. »
George E. P. Box, 1979
22
24. - Side-effects
- Toxicity
- Drug repurposing
Primary target
Secondary
targets
Vast majority of biological targets are proteins (then DNA, RNA, ribosomes,
physicochemical processes)
10% of FDA approved drugs
without known target
Necessity to predict targets of small molecules
24
Primary and Secondary Targets
26. ‘Classical’
Screening:
Library of molecules
with unknown
activities
List of molecules
possibly active on
the same target
Similarity
Molecule with
known activity
‘Inverse’
Screening
(target
predic*on):
List of possible
activities
Similarity
Molecule with
unknown activity
Library of molecules
with known
activities
26
SwissTargetPrediction. Inverse screening based
on molecular similarity
27. Probability, for a pair of
bioactive molecules
showing a given similarity,
to be active on a common
target
Calculated on 350,000
small molecules on one
among 1654 biological
targets from the scientific
literature
Shape similarity
Gfeller,
D.;
Michielin,
O.;
Zoete,
V.
Shaping
the
Interac*on
Landscape
of
Bioac*ve
Molecules.
Bioinforma)cs
2013,
29,
3073–3079.
27
Drug similarity and common biological target
30. Gfeller,
D.;
Grosdidier,
A.;
Wirth,
M.;
Daina,
A.;
Michielin,
O.;
Zoete,
V.
SwissTargetPredic*on:
a
Web
Server
for
Target
Predic*on
of
Bioac*ve
Small
Molecules.
Nucleic
Acids
Res.
2014.
30
SwissTargetPrediction.ch – web interface
32. Olivier Michielin
Vincent Zoete
Christophe Bovigny
Prasad Chaskar
Michel Cuendet
Antoine Daina
David Gfeller
Dennis Haake
Melita Irving
Justyna Iwaszkiewicz
Maria Johansson
Nahzli Dilek
Somi Reddy Majjigapu
Ute Röhrig
Thierry Schuepbach
Prasad
Melita
Somi
Thierry
Ute Justyna
Michel
Maria
David
Antoine
Olivier Vincent
Molecular Modeling Group
Workshop : http://www.atelier-drug-design.ch
All information : http://www.molecular-modelling.ch
Complete list of CADD tools : http://www.click2drug.org
Questions, comments, feedbacks : antoine.daina@isb-sib.ch
vincent.zoete@isb-sib.ch
Nahzli
Dennis
Christophe
33. Empirical Log P method 1: Fragmental/Atomic.
• Drawbacks:
– Additive nature è overestimation with increased MW.
– Fragmental systems cannot cover the entire chemical space è dummy fragments.
– High dependence to training è narrow validity domain, overfitting.
• Molecules similar to those used to build the model è reliable prediction
• Molecules different to the one used to build the model è less reliable prediction
Principle:
1. cutting molecule into
fragments
(evt atoms)
2. retrieving contributions
in established fragmental
system
3. Addition of fragmental
values (evt corrective
factors).
-0,09
-0,00
O
-0,14
NH
-0,22
0,02
0,33
0,33
0,02
OH
0,01
0,33
0,33
fragmental system: atomic contribution:
Σ
+/-
corrective
factors
Predicted
log
P
=
0.91
33
34. Empirical Log P methods 2: Topological
Principle:
1. Description of molecule according to descriptor related to topology
(evt count of atoms).
2. Statistical model (MLR, Neural Network, PLS) è Prediction
• MANNHOLD = 0.11(NC-NHET) + 1.46
• MLOGP =1.244(CX)0.6 - 1.017(NO)0.9 + 0.406(PRX) - 0.145(UB)0.8 + 0.511(HB) +
0.268(POL) - 2.215(AMP) + 0.912(ALK) - 0.392(RNG) - 3.684(QN) + 0.474(NO2) +
1.582(NCS) + 0.773(BLM) - 1.041
– 13 terms among, among which ambiguous ones...
• PRX, For each two atoms directly bonded to each other, add 2.0 and for each two atoms connected via a carbon,
sulfur, or phosphorus atom, add 1.0 unless one of the two bonds connecting the two atoms is a double bond, in
which case, according to some examples in the papers, you must add 2.0. In addition, for each carboxamide group,
we add an extra 1.0 and for each sulfonamide group, we add 2.0.
• HB, a dummy variable if there are any structural features that will create an internal H-bond.
• POL, the number of heteroatoms connected to an aromatic ring by just one bond or the number of carbon atoms
attached to two or more heteroatoms which are also attached to an aromatic ring by just one bond.
• Drawbacks:
– Very high dependence to training è narrow validity domain, overfitting.
– Fitting parameters can lack physical or chemical meaning.
– Very low chemical interpretability è Drug Design.
– Low reproducibility of diverse implementations.
34
35. S
O O
HN
R
OH
Medicinal Chemistry: Problematic fragments
1Brenk, R. et al., 2008. ChemMedChem, 3(3), pp.435–444
2Baell, J.B. & Holloway, G.A., 2010. J. Med. Chem., 53(7), pp.2719–2740.
• Toxic, chemically reactive, metabolically unstable, aggregator, dye or other perturbator of assays.
• 2 complementary filters:
• Brenk1 (Structural alert): List knowledge-based of 150 unwanted fragments to design
academic library for neglected diseases.
• PAINS2 (Pan Assay Interference compounds from Screening): Statistical analysis of
frequent hitters (promiscuous compounds) and breakdown in 490 substructures.
• Real-life usefulness:
35
Promiscuous (PAINS) fragment
if enrichment ≥ 30%
S
O O
HN
OH
R
36. SwissADME: PK Classification Models
• Support Vector Machine (SVM)
Patterns recognition algorithm to perform:
– Classification: two (or more) groups of objects divided by a gap as wide
as possible.
– Prediction: new objects mapped onto that same space and predicted to
which group they belong.
Linear SVM
• To define the optimal hyperplane
showing the maximal margin with the
support vectors ¢.
• In most case, not possible to separate
all member of classes è permissivity
by soft margin controlled by C.
• In most case, better to map objects in
a transformed space. Every dots are
replaced by eg. a Gaussian kernel
function è non-linear SVM
controlled by ϒ.
¢
¢
¢
¢
36
37. Building SVM model for P-gp substrate
• SVM built with MLPy librairy1, RBF kernel (Gaussian).
• Objects / groups: training set (130 P-gp substrates, 81 P-gp non-substrate [n=211])
test set (74 P-gp substrates, 45 P-gp non-substrates [n=119])
• Available descriptors: 35 SwissADME descriptors.
1. Albanese D et al. (2012) mlpy: Machine Learning Python. arXiv.
2. Bikadi Z et al. (2011). PLoS ONE 6:e25815.
3. Wang Z et al. (2011) J Chem Inf Model 51:1447–1456.
37
• P-gp (Permeability glycoprotein-1)
– Active a variety of substrates
across cellular membranes:
– impacts PK (îintestinal
absorption, îCNS permeation)
• Best descriptors : #db_bonds, #atoms, #HB_acc, #HB_don, #rotors, mlogp.
Model
ACC(loo)
MCC(loo)
ACC(ext)
MCC(ext)
SwissADME
0.80
0.57
0.79
0.54
Bikadi2
0.80
0.60
0.76
0.52
Wang3
0.74
0.54
0.88
0.73
38. SVM model for P-gp substrate: Preliminary results
• Best descriptors : #db_bonds, #atoms, #HB_acc, #HB_don, #rotors, mlogp.
• TP: #true positives / TN: #true positives / FP: #false positives / FN: #false negatives
• MCC (Matthew Correlation Coefficient):
– from: -1 (worst prediction) to +1 (perfect prediction).
– MCC > 0.4 for a 2-class to considered predictive.
• Model in development, to be refined...
1. Bikadi Z et al. (2011). PLoS ONE 6:e25815.
2. Wang Z et al. (2011) J Chem Inf Model 51:1447–1456.
Model
ACC(loo)
MCC(loo)
ACC(ext)
MCC(ext)
SwissADME
0.80
0.57
0.79
0.54
Bikadi1
0.80
0.60
0.76
0.52
Wang2
0.74
0.54
0.88
0.73
38