Using computational models like pharmacophores and machine learning, researchers developed in silico models to predict interactions of drugs and compounds with important human drug transporters. Pharmacophore models of P-gp, ASBT, and OCTN2 were able to retrieve known substrates and inhibitors from databases and discover new interacting drug classes. A Bayesian model for ASBT performed well in classification, though external test sets remained challenging. Transporter models aid understanding of absorption, distribution, and toxicity of drugs.
Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target va...Guide to PHARMACOLOGY
The Guide to Pharmacology database (GtoPdb) provides expertly curated information on drug-protein interactions and targets of approved and investigational drugs. It currently includes data on interactions between over 1400 protein targets and 7700 ligands derived from over 5000 literature references. The database covers major target classes and provides a useful resource for target validation and drug discovery. Future plans include regular updates with new target and drug data as well as potential specialty sub-portals within the database.
Systems Pharmacology as a tool for future therapy development: a feasibility ...Guide to PHARMACOLOGY
This study explores using a systems pharmacology approach to analyze the mevalonate branch of the cholesterol biosynthesis pathway. Kinetic parameters and inhibitors of the pathway enzymes were identified from literature and databases. An ordinary differential equation model of the pathway was built and used to predict the optimal drug combination that would suppress production of the cholesterol precursor squalene while maintaining production of geranylgeranyl-PP. The predicted multi-drug approach required a lower total dose than treatment with the statin drug rosuvastatin alone. However, the study found that incomplete and ambiguous pathway data as well as errors in databases currently limit full potential of systems pharmacology approaches.
Computational modeling in drug dispositionSUJITHA MARY
This document discusses computational modeling techniques for predicting drug disposition properties. It covers modeling approaches for drug absorption, distribution, and excretion. For absorption, it describes models for predicting solubility, intestinal permeability, and transporters. For distribution, it discusses models for volume of distribution, plasma protein binding, and blood-brain barrier permeability. For excretion, it summarizes models for hepatic and renal clearance. Current challenges include incorporating active transporters and generating predictive models from physiological understanding rather than empirical correlations.
Computational Modeling of Drug Disposition bhupenkalita7
This document discusses in silico modeling techniques for predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of drug candidates. It describes quantitative approaches like pharmacophore modeling and docking studies, as well as qualitative approaches like quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. Specific techniques are discussed for modeling various ADMET properties like solubility, permeability, plasma protein binding, blood-brain barrier penetration, and clearance. Transporters, ionization, and data quality are also mentioned as important factors. Commercial software packages are noted that can simulate these processes.
Computational modeling in drug dispositionHimal Barakoti
The document discusses computational modeling of drug disposition. It covers modeling of drug absorption, distribution, excretion, and active transport. For drug absorption, it describes modeling of solubility, intestinal permeability, and transporters involved. It also discusses modeling approaches for distribution processes like volume of distribution, plasma protein binding, and blood-brain barrier permeability. Current challenges include better incorporating the effects of active transporters in models. The document emphasizes that while computational models are useful for predicting drug properties, fully accounting for complex biological factors remains difficult.
Myself Omkar Tipugade , M - Pharm sem II , department of Pharmaceutics , today will upload presentation on Computational modeling in drug disposition .
Gastrointestinal absorption simulation using in silico methodology; by Dr. Bh...bhupenkalita7
This PPT includes a brief introduction of in silico models for simulation of GI absorption of drugs, principles involved in the dvelopment of computational models for in silico pharmacokinetic studies related to absorption of drugs from GI tract.
Using computational models like pharmacophores and machine learning, researchers developed in silico models to predict interactions of drugs and compounds with important human drug transporters. Pharmacophore models of P-gp, ASBT, and OCTN2 were able to retrieve known substrates and inhibitors from databases and discover new interacting drug classes. A Bayesian model for ASBT performed well in classification, though external test sets remained challenging. Transporter models aid understanding of absorption, distribution, and toxicity of drugs.
Drug-to-protein mappings in the Guide to PHARMACOLOGY: Utility as a target va...Guide to PHARMACOLOGY
The Guide to Pharmacology database (GtoPdb) provides expertly curated information on drug-protein interactions and targets of approved and investigational drugs. It currently includes data on interactions between over 1400 protein targets and 7700 ligands derived from over 5000 literature references. The database covers major target classes and provides a useful resource for target validation and drug discovery. Future plans include regular updates with new target and drug data as well as potential specialty sub-portals within the database.
Systems Pharmacology as a tool for future therapy development: a feasibility ...Guide to PHARMACOLOGY
This study explores using a systems pharmacology approach to analyze the mevalonate branch of the cholesterol biosynthesis pathway. Kinetic parameters and inhibitors of the pathway enzymes were identified from literature and databases. An ordinary differential equation model of the pathway was built and used to predict the optimal drug combination that would suppress production of the cholesterol precursor squalene while maintaining production of geranylgeranyl-PP. The predicted multi-drug approach required a lower total dose than treatment with the statin drug rosuvastatin alone. However, the study found that incomplete and ambiguous pathway data as well as errors in databases currently limit full potential of systems pharmacology approaches.
Computational modeling in drug dispositionSUJITHA MARY
This document discusses computational modeling techniques for predicting drug disposition properties. It covers modeling approaches for drug absorption, distribution, and excretion. For absorption, it describes models for predicting solubility, intestinal permeability, and transporters. For distribution, it discusses models for volume of distribution, plasma protein binding, and blood-brain barrier permeability. For excretion, it summarizes models for hepatic and renal clearance. Current challenges include incorporating active transporters and generating predictive models from physiological understanding rather than empirical correlations.
Computational Modeling of Drug Disposition bhupenkalita7
This document discusses in silico modeling techniques for predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of drug candidates. It describes quantitative approaches like pharmacophore modeling and docking studies, as well as qualitative approaches like quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. Specific techniques are discussed for modeling various ADMET properties like solubility, permeability, plasma protein binding, blood-brain barrier penetration, and clearance. Transporters, ionization, and data quality are also mentioned as important factors. Commercial software packages are noted that can simulate these processes.
Computational modeling in drug dispositionHimal Barakoti
The document discusses computational modeling of drug disposition. It covers modeling of drug absorption, distribution, excretion, and active transport. For drug absorption, it describes modeling of solubility, intestinal permeability, and transporters involved. It also discusses modeling approaches for distribution processes like volume of distribution, plasma protein binding, and blood-brain barrier permeability. Current challenges include better incorporating the effects of active transporters in models. The document emphasizes that while computational models are useful for predicting drug properties, fully accounting for complex biological factors remains difficult.
Myself Omkar Tipugade , M - Pharm sem II , department of Pharmaceutics , today will upload presentation on Computational modeling in drug disposition .
Gastrointestinal absorption simulation using in silico methodology; by Dr. Bh...bhupenkalita7
This PPT includes a brief introduction of in silico models for simulation of GI absorption of drugs, principles involved in the dvelopment of computational models for in silico pharmacokinetic studies related to absorption of drugs from GI tract.
This document summarizes key transporters involved in drug absorption and disposition:
- Apical sodium dependent bile acid transporter (ASBT) transports bile acids and drug analogs to assist intestinal absorption, with a pharmacophore model revealing requirements of one hydrogen bond donor, one acceptor, one negative charge, and three hydrophobic centers.
- Organic cation transporters (OCTs) facilitate uptake of cationic drugs in various tissues, with a human OCT1 model suggesting requirements of three hydrophobic features and one positive ionizable feature.
- Organic anion transporting polypeptides (OATPs) influence drug plasma concentrations by transporting substrates in multiple tissues, with an OATP1B1 model identifying hydrophobic
Computational modeling of drug distribution jaatinpubg
This document discusses computational modeling techniques for predicting drug distribution properties. It covers two main modeling approaches: quantitative approaches like pharmacophore modeling and docking to study drug-target interactions, and qualitative approaches like QSAR and QSPR studies that use multivariate analysis to correlate molecular descriptors with properties. Key aspects of drug distribution addressed include volume of distribution, plasma protein binding, and blood-brain barrier permeability. The challenges of developing accurate predictive models for these properties are also noted.
The CCK-8 cell viability assay showed that wortmannin has a dose-dependent cytotoxic effect on DU-145 prostate cancer cells, with an IC50 of approximately 100 nM. Flow cytometry analysis of cells treated with 100 nM wortmannin found a decrease in single activation of the PI3K pathway, an increase in dual PI3K/MAPK pathway activation, and a small increase in MAPK pathway activation. Phase contrast images suggest the cells underwent apoptosis rather than lysis in response to wortmannin, as there was little cell debris present. These results indicate wortmannin affects signaling cross-talk in DU-145 cells and may increase apoptosis through impacts on the MAPK and dual PI3
This document presents information about biomarkers from a presentation given by Ms. Suruchi Ramkumar Sharma. It defines biomarkers as objectively measurable indicators of biological states or conditions. Biomarkers can help with early disease diagnosis, drug development, and determining drug effectiveness and safety. Various assay techniques are used to discover biomarkers, including metabolomics approaches to identify indicators of organ toxicity. Specific examples describe biomarkers for liver and kidney toxicity identified through nuclear magnetic resonance spectroscopy and mass spectrometry analyses of biological samples.
This document discusses drug distribution and excretion. It describes how drugs are subjected to disposition processes like distribution and excretion. Distribution involves the reversible transfer of drugs between compartments like blood and tissues. Excretion irreversibly transfers drugs from the body to the external environment through organs like the kidneys, lungs, saliva and milk. The principal types of excretion are renal and hepatic. Computational modeling is used to study drug movement and absorption in order to reduce drug costs.
Transporters play an important role in drug absorption and distribution. Several computational models have been generated to predict transporter interactions and understand substrate requirements. For the P-glycoprotein transporter, models identified two hydrophobic features, two hydrogen bond acceptors, and molecular dimension as essential determinants. Breast cancer resistance protein models emphasize a double bond in ring C and hydroxylation at position 5. Organic cation transporter models found hydrogen bonding features distinguish the two orthologs.
Stanley B. Kahler has over 30 years of experience in analytical chemistry and clinical research coordination. He received his Bachelor of Arts in Chemistry from Union College in 1989. Since 2009, he has worked as a Clinical Research Coordinator at Clinical Trials Management Resources, LLC. Prior to that, he held analytical chemistry roles at several pharmaceutical companies focusing on analytical development and services. He has coordinated multiple phase 3 clinical trials for conditions like osteoarthritis and gout.
This document provides information about the 12th Annual Conference and Exhibition on ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) taking place from June 12-14, 2017 in London. The conference will address early ADME application strategies and discuss the latest screening and testing models. It will feature talks from industry leaders on topics including predictive toxicity, PK optimization, preclinical testing, drug screening technologies, and physiologically-based PK modeling. A workshop on drug transporters will also be held on the third day.
Rushikesh Shinde presented on computational modeling of drug disposition at Alard College of Pharmacy. The presentation discussed how modeling absorption, solubility, and intestinal permeation can help predict drug behavior in the body. Historically, drug candidates often failed in late-stage clinical trials due to issues related to metabolism, excretion, and toxicity, which computational modeling seeks to evaluate earlier in the drug development process. The presentation covered techniques like quantitative structure-activity relationship analysis and pharmacokinetic modeling to computationally simulate drug properties.
This document discusses bioavailability and bioequivalence studies. It provides details on key pharmacokinetic parameters like AUC, Cmax, and Tmax that are evaluated in bioequivalence studies to determine if a generic drug is equivalent to a brand name drug. The document outlines current bioequivalence requirements set by various regulatory agencies like FDA, Health Canada, and others. It also discusses study design considerations, statistical analysis methods, and validation of bioanalytical methods used to evaluate bioequivalence.
This document discusses computational modeling techniques used in drug disposition modeling. It describes two main modeling approaches: quantitative approaches like pharmacophore modeling and docking studies; and qualitative approaches like QSAR and QSPR studies. It then discusses how these techniques can be applied to model key aspects of drug disposition, including absorption, distribution, and excretion. The key aspects of drug absorption, distribution, and excretion are also summarized.
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Computational modelling of drug disposition active transportSUJITHA MARY
This document discusses computational modeling of active transport mechanisms that influence drug disposition. It summarizes modeling efforts for several major drug transporters, including P-glycoprotein (P-gp), Breast Cancer Resistance Protein (BCRP), nucleoside transporters, peptide transporter 1 (hPEPT1), Apical Sodium-dependent Bile Acid Transporter (ASBT), Organic Cation Transporters (OCTs), Organic Anion Transporting Polypeptides (OATPs), and the Blood Brain Barrier choline transporter. While transporter modeling has advanced, fully incorporating active transport into predictive models remains an ongoing challenge.
This document summarizes recent processes developed for constructing homogeneous antibody-drug conjugates (ADCs). It discusses three categories of approaches: 1) engineering amino acids in antibodies to introduce unique conjugation sites, 2) using enzymes to modify antibodies, and 3) modifying drug linkers. Several examples are provided for each approach, including payloads, drug-to-antibody ratios achieved, and advantages over conventional heterogeneous ADCs. The document concludes that while homogeneous ADCs show improved properties, recombinant engineering methods may not be applicable to existing approved antibodies, and clinical performance of homogeneous ADCs remains to be confirmed.
This quick start guide summarizes the typical 7 step workflow for using the GenoCMS gene-centric content management system: 1) log in as a guest, 2) view the default protein set, 3) change display settings to select relevant tracks, 4) select proteins for a new user set, 5) delete proteins from the user set, 6) switch to table view and change identifier settings, 7) export the data. It also describes registering to share user sets with other users.
In vitro screening for evaluation of drugs ADMET propertiesdilip kumar tampula
The document discusses pre-clinical in vitro screening techniques used to evaluate drugs' absorption, distribution, metabolism, excretion and toxicity (ADMET) properties early in the drug discovery process. It describes assays for various ADMET properties including partition coefficient, aqueous solubility, metabolic stability, plasma protein binding, and toxicity. The assays allow rapid evaluation of compounds with low amounts of material and help identify those with favorable pharmacokinetic and safety profiles to progress in development. The goal is to incorporate ADMET screening earlier to simultaneously optimize all drug properties.
The document discusses the PK/PD reporting and analysis services provided by Pharsight Corporation. It outlines Pharsight's capabilities including software products for drug development data analysis, strategic consulting services, metadata modeling, and training. It then describes Pharsight's PK/PD reporting and analysis services which include non-compartmental analysis, population PK/PD analysis, clinical study report writing, and biostatistics. Specific applications of modeling and simulations are also summarized such as structural model discrimination, simulations according to different scenarios, optimal sampling strategies, and sparse sampling assessments.
This document summarizes a seminar on computational methods for drug disposition. It discusses two approaches to modeling drug disposition: qualitative and quantitative. The quantitative approach uses pharmacophore modeling and docking to study drug interactions, while the qualitative approach uses QSAR and QSPR to correlate molecular descriptors with ADMET properties. The document also reviews the key processes of drug disposition: absorption, distribution, metabolism, and excretion. It provides examples of two research articles, one on the placental disposition of the immunosuppressant tacrolimus, and another on the pharmacokinetics of miltefosine in mice and hamsters infected with Leishmania.
The document describes using RT2Profiler PCR arrays to identify tumor-specific genes by comparing gene expression profiles between tumor and normal tissue samples. Key points:
1) 33 genes were found to have at least a 3-fold difference in expression between a breast tumor sample and normal breast tissue using the Cancer PathwayFinder array.
2) Seven of these genes code for cellular adhesion molecules. Further analysis with an extracellular matrix array identified 38 adhesion-related genes with differential expression.
3) The PCR arrays allow easy, reproducible, and sensitive identification of differentially expressed genes between tumor types or conditions through real-time PCR analysis of focused gene sets.
Integrating Pathway Information with Gene Expression Data to Identify Novel ...Charlie Pei
This document describes a study that integrated pathway information with gene expression data from the Connectivity Map database to identify novel pathway-specific cancer drugs. The study focused on four major cancer pathways: p53 signaling, PI3K/AKT signaling, PTEN signaling, and Wnt/β-catenin signaling. A novel method was developed to calculate pathway enrichment scores and identify drugs that significantly affect the pathways. Several known cancer drugs were validated, and some potential new cancer drug indications were predicted, though none were found for the Wnt pathway. Future work could improve the method and integrate additional databases to further analyze drug effects on pathways.
This document discusses the application of clinical proteomics in disease diagnosis and biomarker discovery. It provides an overview of how proteomics methodologies like mass spectrometry and protein microarrays can be used to identify protein biomarkers for various diseases from body fluids. Specific examples are given of proteomics studies that have discovered protein biomarker patterns or specific proteins that can improve diagnosis of cancers like colorectal cancer and breast cancer compared to single biomarkers. Biomarkers identified for other diseases like Alzheimer's disease and diabetic nephropathy through proteomics are also summarized.
Several Types of PROTACs Based On Nucleic AcidsDoriaFang
So far, more than 10 nucleic acid drugs have been approved for marketing worldwide, and many nucleic acid drugs are in the stage of clinical trials. Nucleic acid drugs are expected to become the third type of drugs after small molecule drugs and antibody drugs.
This document summarizes key transporters involved in drug absorption and disposition:
- Apical sodium dependent bile acid transporter (ASBT) transports bile acids and drug analogs to assist intestinal absorption, with a pharmacophore model revealing requirements of one hydrogen bond donor, one acceptor, one negative charge, and three hydrophobic centers.
- Organic cation transporters (OCTs) facilitate uptake of cationic drugs in various tissues, with a human OCT1 model suggesting requirements of three hydrophobic features and one positive ionizable feature.
- Organic anion transporting polypeptides (OATPs) influence drug plasma concentrations by transporting substrates in multiple tissues, with an OATP1B1 model identifying hydrophobic
Computational modeling of drug distribution jaatinpubg
This document discusses computational modeling techniques for predicting drug distribution properties. It covers two main modeling approaches: quantitative approaches like pharmacophore modeling and docking to study drug-target interactions, and qualitative approaches like QSAR and QSPR studies that use multivariate analysis to correlate molecular descriptors with properties. Key aspects of drug distribution addressed include volume of distribution, plasma protein binding, and blood-brain barrier permeability. The challenges of developing accurate predictive models for these properties are also noted.
The CCK-8 cell viability assay showed that wortmannin has a dose-dependent cytotoxic effect on DU-145 prostate cancer cells, with an IC50 of approximately 100 nM. Flow cytometry analysis of cells treated with 100 nM wortmannin found a decrease in single activation of the PI3K pathway, an increase in dual PI3K/MAPK pathway activation, and a small increase in MAPK pathway activation. Phase contrast images suggest the cells underwent apoptosis rather than lysis in response to wortmannin, as there was little cell debris present. These results indicate wortmannin affects signaling cross-talk in DU-145 cells and may increase apoptosis through impacts on the MAPK and dual PI3
This document presents information about biomarkers from a presentation given by Ms. Suruchi Ramkumar Sharma. It defines biomarkers as objectively measurable indicators of biological states or conditions. Biomarkers can help with early disease diagnosis, drug development, and determining drug effectiveness and safety. Various assay techniques are used to discover biomarkers, including metabolomics approaches to identify indicators of organ toxicity. Specific examples describe biomarkers for liver and kidney toxicity identified through nuclear magnetic resonance spectroscopy and mass spectrometry analyses of biological samples.
This document discusses drug distribution and excretion. It describes how drugs are subjected to disposition processes like distribution and excretion. Distribution involves the reversible transfer of drugs between compartments like blood and tissues. Excretion irreversibly transfers drugs from the body to the external environment through organs like the kidneys, lungs, saliva and milk. The principal types of excretion are renal and hepatic. Computational modeling is used to study drug movement and absorption in order to reduce drug costs.
Transporters play an important role in drug absorption and distribution. Several computational models have been generated to predict transporter interactions and understand substrate requirements. For the P-glycoprotein transporter, models identified two hydrophobic features, two hydrogen bond acceptors, and molecular dimension as essential determinants. Breast cancer resistance protein models emphasize a double bond in ring C and hydroxylation at position 5. Organic cation transporter models found hydrogen bonding features distinguish the two orthologs.
Stanley B. Kahler has over 30 years of experience in analytical chemistry and clinical research coordination. He received his Bachelor of Arts in Chemistry from Union College in 1989. Since 2009, he has worked as a Clinical Research Coordinator at Clinical Trials Management Resources, LLC. Prior to that, he held analytical chemistry roles at several pharmaceutical companies focusing on analytical development and services. He has coordinated multiple phase 3 clinical trials for conditions like osteoarthritis and gout.
This document provides information about the 12th Annual Conference and Exhibition on ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) taking place from June 12-14, 2017 in London. The conference will address early ADME application strategies and discuss the latest screening and testing models. It will feature talks from industry leaders on topics including predictive toxicity, PK optimization, preclinical testing, drug screening technologies, and physiologically-based PK modeling. A workshop on drug transporters will also be held on the third day.
Rushikesh Shinde presented on computational modeling of drug disposition at Alard College of Pharmacy. The presentation discussed how modeling absorption, solubility, and intestinal permeation can help predict drug behavior in the body. Historically, drug candidates often failed in late-stage clinical trials due to issues related to metabolism, excretion, and toxicity, which computational modeling seeks to evaluate earlier in the drug development process. The presentation covered techniques like quantitative structure-activity relationship analysis and pharmacokinetic modeling to computationally simulate drug properties.
This document discusses bioavailability and bioequivalence studies. It provides details on key pharmacokinetic parameters like AUC, Cmax, and Tmax that are evaluated in bioequivalence studies to determine if a generic drug is equivalent to a brand name drug. The document outlines current bioequivalence requirements set by various regulatory agencies like FDA, Health Canada, and others. It also discusses study design considerations, statistical analysis methods, and validation of bioanalytical methods used to evaluate bioequivalence.
This document discusses computational modeling techniques used in drug disposition modeling. It describes two main modeling approaches: quantitative approaches like pharmacophore modeling and docking studies; and qualitative approaches like QSAR and QSPR studies. It then discusses how these techniques can be applied to model key aspects of drug disposition, including absorption, distribution, and excretion. The key aspects of drug absorption, distribution, and excretion are also summarized.
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Computational modelling of drug disposition active transportSUJITHA MARY
This document discusses computational modeling of active transport mechanisms that influence drug disposition. It summarizes modeling efforts for several major drug transporters, including P-glycoprotein (P-gp), Breast Cancer Resistance Protein (BCRP), nucleoside transporters, peptide transporter 1 (hPEPT1), Apical Sodium-dependent Bile Acid Transporter (ASBT), Organic Cation Transporters (OCTs), Organic Anion Transporting Polypeptides (OATPs), and the Blood Brain Barrier choline transporter. While transporter modeling has advanced, fully incorporating active transport into predictive models remains an ongoing challenge.
This document summarizes recent processes developed for constructing homogeneous antibody-drug conjugates (ADCs). It discusses three categories of approaches: 1) engineering amino acids in antibodies to introduce unique conjugation sites, 2) using enzymes to modify antibodies, and 3) modifying drug linkers. Several examples are provided for each approach, including payloads, drug-to-antibody ratios achieved, and advantages over conventional heterogeneous ADCs. The document concludes that while homogeneous ADCs show improved properties, recombinant engineering methods may not be applicable to existing approved antibodies, and clinical performance of homogeneous ADCs remains to be confirmed.
This quick start guide summarizes the typical 7 step workflow for using the GenoCMS gene-centric content management system: 1) log in as a guest, 2) view the default protein set, 3) change display settings to select relevant tracks, 4) select proteins for a new user set, 5) delete proteins from the user set, 6) switch to table view and change identifier settings, 7) export the data. It also describes registering to share user sets with other users.
In vitro screening for evaluation of drugs ADMET propertiesdilip kumar tampula
The document discusses pre-clinical in vitro screening techniques used to evaluate drugs' absorption, distribution, metabolism, excretion and toxicity (ADMET) properties early in the drug discovery process. It describes assays for various ADMET properties including partition coefficient, aqueous solubility, metabolic stability, plasma protein binding, and toxicity. The assays allow rapid evaluation of compounds with low amounts of material and help identify those with favorable pharmacokinetic and safety profiles to progress in development. The goal is to incorporate ADMET screening earlier to simultaneously optimize all drug properties.
The document discusses the PK/PD reporting and analysis services provided by Pharsight Corporation. It outlines Pharsight's capabilities including software products for drug development data analysis, strategic consulting services, metadata modeling, and training. It then describes Pharsight's PK/PD reporting and analysis services which include non-compartmental analysis, population PK/PD analysis, clinical study report writing, and biostatistics. Specific applications of modeling and simulations are also summarized such as structural model discrimination, simulations according to different scenarios, optimal sampling strategies, and sparse sampling assessments.
This document summarizes a seminar on computational methods for drug disposition. It discusses two approaches to modeling drug disposition: qualitative and quantitative. The quantitative approach uses pharmacophore modeling and docking to study drug interactions, while the qualitative approach uses QSAR and QSPR to correlate molecular descriptors with ADMET properties. The document also reviews the key processes of drug disposition: absorption, distribution, metabolism, and excretion. It provides examples of two research articles, one on the placental disposition of the immunosuppressant tacrolimus, and another on the pharmacokinetics of miltefosine in mice and hamsters infected with Leishmania.
The document describes using RT2Profiler PCR arrays to identify tumor-specific genes by comparing gene expression profiles between tumor and normal tissue samples. Key points:
1) 33 genes were found to have at least a 3-fold difference in expression between a breast tumor sample and normal breast tissue using the Cancer PathwayFinder array.
2) Seven of these genes code for cellular adhesion molecules. Further analysis with an extracellular matrix array identified 38 adhesion-related genes with differential expression.
3) The PCR arrays allow easy, reproducible, and sensitive identification of differentially expressed genes between tumor types or conditions through real-time PCR analysis of focused gene sets.
Integrating Pathway Information with Gene Expression Data to Identify Novel ...Charlie Pei
This document describes a study that integrated pathway information with gene expression data from the Connectivity Map database to identify novel pathway-specific cancer drugs. The study focused on four major cancer pathways: p53 signaling, PI3K/AKT signaling, PTEN signaling, and Wnt/β-catenin signaling. A novel method was developed to calculate pathway enrichment scores and identify drugs that significantly affect the pathways. Several known cancer drugs were validated, and some potential new cancer drug indications were predicted, though none were found for the Wnt pathway. Future work could improve the method and integrate additional databases to further analyze drug effects on pathways.
This document discusses the application of clinical proteomics in disease diagnosis and biomarker discovery. It provides an overview of how proteomics methodologies like mass spectrometry and protein microarrays can be used to identify protein biomarkers for various diseases from body fluids. Specific examples are given of proteomics studies that have discovered protein biomarker patterns or specific proteins that can improve diagnosis of cancers like colorectal cancer and breast cancer compared to single biomarkers. Biomarkers identified for other diseases like Alzheimer's disease and diabetic nephropathy through proteomics are also summarized.
Several Types of PROTACs Based On Nucleic AcidsDoriaFang
So far, more than 10 nucleic acid drugs have been approved for marketing worldwide, and many nucleic acid drugs are in the stage of clinical trials. Nucleic acid drugs are expected to become the third type of drugs after small molecule drugs and antibody drugs.
BioExpo 2023 Presentation - Computational Chemistry in Drug Discovery: Bridgi...Trustlife
Computational methods were used to develop novel inhibitors of protein kinases involved in diseases like diabetes and cancer. Computational tools like molecular docking, QSAR modeling, and molecular dynamics simulations were employed to identify potential inhibitors. Several analogs of a JNK inhibitor were designed and tested experimentally, with some analogs showing improved activity against HepG2 cells. Computational drug design techniques hold promise for developing new treatments for diseases like nonalcoholic fatty liver disease that currently lack effective pharmaceutical therapies.
QPS Regulated Bioanalysis of Antibody Drug ConjugatesQPS Holdings, LLC
PK profiling reflects molecular complexity. Since 2001 QPS’ bioanalytical teams have contributed to ADC drug development,
supporting the filing of one of the first drug targeting
programs and continue to develop customized
strategies for novel conjugate molecules.
The Future Development of ADC For Cancer.pdfDoriaFang
Antibody-drug conjugates (ADCs) combine monoclonal antibodies with cytotoxic drugs to selectively deliver drugs to tumor sites. ADCs have shown promise in treating cancer but have faced challenges developing therapies for solid tumors. Future development of ADCs includes novel tumor targets, payloads with new mechanisms of action, improved linker techniques, and new antibody forms. While innovations still need validation, research has provided encouraging results, and ADCs are expected to have an increasingly important role in cancer treatment over the next decade.
Introduction to the drug discovery processThanh Truong
This document discusses the drug discovery process from target identification through FDA approval. It describes methods used for target identification such as genomics, bioinformatics, and proteomics. The stages of lead identification through high-throughput screening and structure-based drug design are outlined. Key aspects of lead optimization like characterizing potency, efficacy, pharmacokinetics, and toxicity are summarized. Details are provided on preclinical and clinical trial phases from Phase 0 through Phase IV post-marketing surveillance. Factors contributing to the declining drug approval rate like increased safety demands are noted. The high costs and failure rates associated with drug development are highlighted.
This document summarizes a journal club presentation on C-reactive protein (CRP) testing. The presentation discusses the history and production of CRP, its role as an acute phase reactant, and methods for detecting CRP levels. It specifically compares standard CRP testing to high-sensitivity CRP testing and finds high agreement between the two methods in identifying patients at high risk for cardiovascular events. Reimbursement rates for different CRP tests are also addressed.
1. Several Pancratistatin (PST) analogs, including SVTH-7, SVTH-6, and SVTH-5, were found to have potent anti-cancer activity greater than PST and standard chemotherapeutics in a medium-throughput screen of various cancer cell lines.
2. The PST analogs disrupted mitochondrial function, activated the intrinsic apoptotic pathway, and reduced tumor growth in vivo. Inhibition of mitochondrial complexes II and III abrogated the pro-apoptotic effects of SVTH-7, suggesting it exploits a mitochondrial vulnerability.
3. This work identifies several PST analogs with high therapeutic potential and provides insight into distinct mitochondrial features of cancer
Rational drug design involves developing compounds that target specific biomolecules involved in disease processes through protein-protein or protein-nucleic acid interactions. Protein targets can be identified through techniques like X-ray crystallography and NMR. Computational tools and global gene expression analysis help increase the efficiency and cost-effectiveness of the drug design process by aiding in structure-guided approaches and target identification. Drug design can involve developing ligands for targets with known structures or developing ligands with predefined properties for unknown targets identified through gene expression data. Combination therapies and overcoming toxic side effects are important challenges in developing improved anti-cancer drugs.
This document provides an overview of using gene expression profiling to evaluate drug metabolism-induced toxicity. It discusses how drug metabolism can lead to toxicity and the need to systematically evaluate this in pre-clinical studies. It then describes using Qiagen's RT2 Profiler PCR Arrays, which allow profiling of 84 drug metabolizing enzyme genes, to detect abnormalities in drug metabolism and identify mechanisms of toxicity using human hepatocytes treated with different compounds as an example application. The results showed induction and inhibition of various metabolizing enzyme genes in response to the compounds tested.
Research Avenues in Drug discovery of natural productsDevakumar Jain
This document discusses challenges facing the pharmaceutical industry and opportunities for natural products in drug discovery. The pharmaceutical industry faces losses of patent protection for many drugs, increasing costs, and litigation. Natural products are attractive alternatives as they have evolved to be bioactive and have structures not limited by human design. Advances like high-throughput screening, metabolomics, metagenomics, and metabolic engineering can help access natural product diversity and accelerate drug discovery from natural sources.
The document discusses drug design, development, and delivery. It covers rational drug design using molecular properties and receptor modeling. Computer-assisted drug design uses molecular docking and QSAR methods. Neural networks are also used in drug design. Drug discovery involves identifying candidates and screening for efficacy. Drug development evaluates ADME, toxicity, and safety through preclinical and clinical studies. Drug delivery methods aim to effectively administer pharmaceutical compounds and improve drug release profiles.
2014 11-27 ODDP 2014 course, Amsterdam, Alain van GoolAlain van Gool
Presentation as part of a comprehensive oncology drug development course, to discuss a pharmaceutical approach to identify, validate and develop biomarkers for personalized medicine for melanoma.
This document summarizes a study on developing a targeted nano drug delivery system for treating breast cancer using docetaxel. The objectives are to formulate a docetaxel nanosuspension to improve its bioavailability and target it to cancer cells using antibody drug conjugates. The plan involves preformulation studies, developing and characterizing the nanosuspension, testing release kinetics and cell viability, selecting an optimized formulation, and conducting stability studies. The approach aims to enhance docetaxel's solubility and therapeutic effects while reducing dose and side effects.
Personalized Medicine: Matching cancer drugs with mechanism (AAPS webinar)Anton Yuryev
Anton Yuryev describes how to identify optimal molecular targets and drugs for personalized cancer treatment using network and pathway analysis of transcriptomics profiles from tumor biopsies. The approach involves determining the most active targets using network analysis, finding cancer hallmark pathways enriched with these targets, and identifying FDA-approved drugs targeting the most active hallmarks. Sub-Network Enrichment Analysis is used to calculate regulator activity from downstream targets in patient profiles. Pathway Studio contains cancer pathway models built from literature to map patient profiles and find druggable targets. The approach is validated for stage IV cancer patients and aims to optimize treatment by targeting multiple identified regulators with drug combinations.
This document discusses genomics and proteomics based drug discovery. It explains that genomics involves sequencing genomes to understand gene functions and interactions, while proteomics studies protein expression and interactions. The document outlines how structural bioinformatics and techniques like protein-ligand docking can help in drug target identification and rational drug design. It also discusses how proteomics can aid in various stages of drug discovery like target identification and validation.
Peptide Drug Conjugates (PDCs) Novel Targeted Therapeutics For Cancer.pdfDoriaFang
Peptide-drug conjugates (PDCs) are the next generation of targeted therapies after ADCs. PDCs has been developed as targeted therapeutic candidates for cancer, COVID-19, metabolic diseases, etc.
International Journal of Pharmaceutical Science Invention (IJPSI)inventionjournals
This study evaluated the clinical utility of total prostate specific antigen (TPSA) and prostatic acid phosphatase (PAP) for distinguishing between prostate carcinoma (PCa) and benign prostatic hyperplasia (BPH) in Sudanese patients. The study analyzed serum levels of TPSA and PAP in 200 patients with PCa or BPH and 100 healthy controls. TPSA levels were significantly higher in PCa patients compared to BPH patients and correlated with cancer severity. PAP levels were also higher in PCa but were less accurate than TPSA, especially at lower TPSA ranges. The results indicate that TPSA can better discriminate PCa from BPH compared to PAP and is
This is the presentation on Role of advancement in instrumentation in pharmacodynamic evaluation of drugs
in clinical trials.
CONTENTS
Concept of medical instrument and instrumentation
Centrifuge
PCR
HPLC
Flow cytometry
Mass SPECTROMETRY
Minimally invasive technologies in PD
Conclusion
Similar to Drug repositioning for hepatocellular carcinoma (20)
Knowledge graph applications for cosmetics industryAnton Yuryev
This document summarizes three use cases for Elsevier's deep reading AI and biology knowledge graph. The first use case identifies UV-absorbing compounds for skin care by annotating over 684,000 compounds with relevant cell processes and diseases. The second use case identifies compounds that can modulate estrogen production by analyzing relevant metabolic pathways and regulation networks. The third use case performs transcriptomics analysis of androgenic alopecia to build a regulatory network model and identify new drug targets using differential expression analysis and sub-network enrichment.
Five drug development strategies to combat SARS-CoV2Anton Yuryev
Slides were presented at webinar on “Opportunities & Challenges in Drug Discovery and Development” organised by Elsevier in collaboration with Dr Reddy’s Institute of Life Sciences, Hyderabad on July 16th,2020
Drugs predicted to bind #COVID19 proteins by computational dockingAnton Yuryev
Drugs predicted by computational ligand docking to bind COVID19 proteins from Wu et al 2020, Kandeel et al 2020, Joshi et al 2020, Adem et al 2020 articles
Genetic variations linked to Acute Respiratory Distress syndromeAnton Yuryev
The list of rs Identifiers linked by to ARDS in peer-reviewed scientific literature. This list can help determine individuals at risk to develop severe symptoms from COVID19 infection
AAK1 GAK inhibitors for anti-COVID19 therapyAnton Yuryev
BenevolentAI has reported three AAK1 and GAK kinase inhibitors effective against #coronavirus #COVID19. I publish the list of 35 approved drugs and lead compounds that can inhibit AAK1 and GAK kinases and therefore can be effective against #COVID19. Drugs were found in #Elsevier #PathwayStudio and #Reaxys knowledgebases. To find more drugs that can be effective against #COVID19 please visit #Elsevier Coronavirus Information Center or read my blog about atrategies to find more drugs for #coronavirus
Drug re-positioning for tuberculosis infection in HIV/ADS patients Anton Yuryev
Tuberculosis infection is #1 cause of death among HIV patients in the developing countries. Drugs must not only inhibit tuberculosis but also inhibit HIV infection. Sophisticated searches in #Elsevier #PathwayStudio knowledge graph revealed that several FDA approved drugs have appropriate therapeutic profile.
Patient microbiome analysis using Elsevier text miningAnton Yuryev
This presentation demonstrates how to use Elsevier Text Mining to analyze a patient's microbiome profile from Aperiomics and interpret the results. Multiple Search is used to identify bacterial species from the patient's throat and stool that have been linked to her lung and bowel problems in medical literature. Species linked to her conditions include Streptococcus pneumoniae, Streptococcus pyogenes, Haemophilus parainfluenzae, and Fusobacterium nucleatum in her throat and Bacteroides vulgatus in her stool. Multiple Search also identifies protective species in her stool like Bifidobacterium longum and Faecalibacterium prausnitzii. Antibiotics and probiotics are then suggested
The document analyzes the tumor transcriptomics profile of a patient with stage IV gallbladder cancer. It identifies the top 200 most significantly activated expression regulators in the patient's tumor using sub-network enrichment analysis software. Key regulators identified include histone deacetylases and DNA methyltransferases. The analysis suggests treatment with the HDAC inhibitor vorinostat and discusses how activated regulators like HDACs, PDCD1, and CTLA4 may be contributing to tumor proliferation and immune evasion. Graphical summaries show pathways enriched with active regulators in the tumor related to cancer hallmarks like histone modification and immune response evasion.
OMICs data analysis using Pathway StudioAnton Yuryev
This document provides summaries of 32 publications from 2018-2019 that used Pathway Studio software for OMICs data analysis. The publications covered a wide range of topics including Alzheimer's disease, stress response genes in Drosophila, gene expression following radiotherapy exposure, miRNA and mRNA profiling in horse satellite cells, and proteome changes in plant somatic embryogenesis. Figures from each publication are also presented.
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
This document compares two approaches for identifying potential gene expression regulators from experimental data: Sub-Network Enrichment Analysis (SNEA) in Pathway Studio and Causal Reasoning in Ingenuity Pathway Analysis (IPA). It analyzes a publicly available dataset on Spinal Muscular Atrophy. SNEA identified biological functions and expression regulators more specifically related to neurogenesis, the key process affected in SMA. It produced results with greater relevance to the disease compared to IPA. Mapping genes to a motor neuron differentiation model also showed SNEA results were more consistent with SMN1 knockout.
Analysis of Functional Magnetic Resonance Imaging (fMRI) data from human brai...Anton Yuryev
The presentation from National Institute of Mental Health shows how to use Pathway Studio data for analysis of fMRI from schizophrenia patients. It finds novel proteins that can be used as biomarkers or drug targets for schizophrenia.
Elsevier helps malaria research with comprehensive Plasmodium biology databaseAnton Yuryev
Elsevier professional services constructed the most comprehensive knowledgebase for network and pathway analysis of Plasmodium genome using deep reading NLP technology from thousands scientific publications about Plasmodium parasite. The database allows pathway reconstruction by data-mining human-parasite protein interactions, network enrichment analysis of OMICs data-sets, search for compounds modulating human immune response to parasite or inhibiting Plasmodium proteins. This slide presentation shows some of key features and statistics for this database.
Presentation at Rare Disease conference in San-AntonioAnton Yuryev
Elsevier has significantly reduced the cost of drug development for rare diseases through drug repurposing. They have brought together knowledge about drug targets, effects, and disease biology to identify drugs and nutraceuticals approved by the FDA that could potentially be repositioned to treat rare diseases, eliminating the need for new drug development and clinical trials. Using automated queries of Elsevier knowledgebases, they can provide summaries of potential treatments for a given rare disease, including key researchers and institutions, relevant drug targets, and approved drugs that may be effective - reducing the cost of repositioning existing drugs to under $500,000.
Profiling how Immune inhibitors Secreted by Melanoma affect NK & other immune...Anton Yuryev
The document summarizes how Elsevier's solutions can help identify novel immunotherapy targets for melanoma by integrating data from multiple sources. It describes using natural language processing to extract information on immune mechanisms from over 20,000 articles, identifying 226 proteins secreted by melanoma that inhibit immune activation and 142 that activate immune tolerance. This approach found many potential immunotherapy targets, including examples of novel targets and opportunities for drug repurposing. Integrating these insights could help create models for combinatorial treatments and better match patients to treatments.
Profiling how Immune inhibitors Secreted by Melanoma affect NK & other immune...Anton Yuryev
The document summarizes how Elsevier's solutions can help identify novel immunotherapy targets for melanoma by integrating data from multiple sources. It describes using natural language processing to extract information on immune mechanisms from over 20,000 articles, identifying 226 proteins secreted by melanoma that inhibit immune activation and 142 that activate immune tolerance. This approach found many potential immunotherapy targets, including examples of novel targets and opportunities for drug repurposing. Integrating these insights could help create models for combinatorial treatments and better match patients to treatments.
This document summarizes a presentation by Timothy Hoctor, VP of Professional Services at Elsevier, about Elsevier's strategic vision and professional services. The key points are:
1) Elsevier aims to increase R&D productivity by linking data across the development spectrum and increase return on information through enhanced search and visualization tools.
2) Elsevier's Professional Services team leverages Elsevier's capabilities to provide customized data management and analysis solutions.
3) Elsevier's strategic objective is to become a leading collaborator in R&D data management through services like data mapping, gap analysis, data governance, and integrated data management.
Pathway analysis for personalized oncologyAnton Yuryev
1) The document discusses using pathway analysis and pathway activity signatures to enable more personalized cancer treatment. It outlines calculating major expression regulators from patient omics data and mapping them to cancer pathways to determine activated pathways.
2) Calculating pathway activity signatures which are short allows better patient classification compared to single targets. Pathway activity also allows selection of drugs that inhibit the active pathway.
3) An example shows a patient's tumor signaling pathway was identified and treatment with drugs targeting the pathway led to no cancer metastasis. The approach aims to continue validating with more medical collaborators.
Construction of cancer pathways for personalized medicineAnton Yuryev
This document discusses constructing cancer pathways for personalized medicine using sub-network enrichment analysis (SNEA). SNEA calculates the transcriptional activity of upstream regulators using differentially expressed genes and regulatory knowledgebases. This identifies key expression regulators in specific cancer patients. Mapping these regulators onto known pathways builds personalized cancer pathways for each patient. Analyzing pathways from multiple patients provides insights into common cancer biology and identifies potential drug targets in a personalized manner.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Drug repositioning for hepatocellular carcinoma
1. Drug repositioning for Hepatocellular
carcinoma
Prepared by Anton Yuryev, Ph.D., Elsevier Professional Services, on February 25th
2017
Disclaimer: Data in Pathway Studio and Reaxys Medicinal Chemistry is regular updated by Elsevier.
Therefore the reproduction of workflow described in this report may produce additional results that will
be different from the results presented in this report.
Contents
Selection of drug targets using “Hepatocellular Carcinoma Overview”.......................................................1
Figure 1: Location of “Hepatocellular Carcinoma Overview” pathway in Pathway Studio ..................2
Figure 2: “Hepatocellular Carcinoma Overview” pathway in Pathway Studio.....................................3
Table 1: List of targets from “Hepatocellular Carcinoma Overview” considered in this project..........3
Identification of polypharmacologic compounds for hepatocellular carcinoma using Reaxys Medicinal
Chemistry (RMC)...........................................................................................................................................4
Figure 3: Screenshot of Reaxys Medicinal Chemistry search interface showing query for all targets
names in Table 1 ...................................................................................................................................4
Figure 4: Reaxys Screenshot showing progressive filtering for polypharmacologic compounds
repositioned for hepatocellular carcinoma ..........................................................................................5
Identification of compounds tested against Hepatocellular Carcinoma using Pathway Studio...........5
Figure 5: 122 RMC compounds from Table 2 found in Pathway Studio, 52 of them have links to
“Hepatocellular Carcinoma” disease entity..........................................................................................5
Table 2: List of unique polypharmacologic compounds from RMC found for “Hepatocellular
Carcinoma Overview” and their relations to Hepatocellular Carcinoma in Pathway Studio. ..............6
Conclusion...................................................................................................... Error! Bookmark not defined.
Appendix A. Bioactivities for polypharmacologic compounds inhibiting multiple targets in
“Hepatocellular Carcinoma Overview”.......................................................... Error! Bookmark not defined.
Selection of drug targets using “Hepatocellular Carcinoma Overview”
“Hepatocellular Carcinoma Overview” disease pathway model is available in Pathway Studio Curated
Pathways “Disease Collections” in “Hepatocellular Carcinoma” folder (see screenshot below).
2. Figure 1: Location of “Hepatocellular Carcinoma Overview” pathway in Pathway Studio
Targets for Hepatocellular Carcinoma disease can be selected by visual inspection of “Hepatocellular
Carcinoma Overview” disease pathway and by reading pathway description available in its “Properties”.
The screenshot below shows “Hepatocellular Carcinoma Overview” as it is available in Pathway Studio.
Proteins with red-highlight are up-regulated in disease; proteins with blue highlight are down-regulated
in disease; proteins shown by white shapes and red border have mutations genetically linked to disease.
The detailed description of curated pathways is available in Pathway Studio Help.
The proteins shown by blue contour on Figure 2 were selected as drug targets. The following criteria for
target selection were used:
1) Targets must be druggable proteins, i.e. have 3D structure suitable for designing drugs. The
following classes of proteins are considered druggable:
a. Proteins that bind small molecules: kinases, metabolic enzymes, GPCRs, phosphatases,
protein modification enzymes, protein channels, proteases, ubiquitin-ligases
b. Secreted and extracellular portions of transmembrane proteins. They can bind antibody
drugs that are unable penetrate a cell for inhibition of intracellular proteins.
2) Targets must be major regulators in disease pathway. The major regulators in “Hepatocellular
Carcinoma Overview” are hormones and their receptors that control different stages of
oncological transformation and cancer growth.
3. Figure 2: “Hepatocellular Carcinoma Overview” pathway in Pathway Studio
From pathway analysis point of view the best drugs must inhibit multiple targets in disease pathway.
The approach for designing multi-target drugs against specific pathway is called polypharmacology.
Antibody drugs target protein hormones that are typically not druggable by small molecular weight
inhibitors. However, antibody drugs have high target selectivity and are not suitable for
polypharmacology. Therefore we chose receptors and proteins directly modifying the receptors in
“Hepatocellular Carcinoma Overview” for drug targeting. List of targets considered for
polypharmacologic repositioning is shown in Table 1 below.
Table 1: List of targets from “Hepatocellular Carcinoma Overview” considered in this project
Connectivity – number of relations in Pathway Studio database, indicates how much information is
available about the target in scientific literature.
Name Connectivity Description
LRP5 647 low density lipoprotein receptor-related protein 5
FZD7 269 frizzled family receptor 7
LRP6 745 low density lipoprotein receptor-related protein 6
FLT1 2218 fms-related tyrosine kinase 1
ERBB2 4372 erb-b2 receptor tyrosine kinase 2
ERBB3 1116 kinase inactive
FLT4 892 fms-related tyrosine kinase 4
ERBB4 1044 v-erb-a erythroblastic leukemia viral oncogene homolog 4 (avian)
4. KDR 3649 kinase insert domain receptor
MET 2937 met proto-oncogene (hepatocyte growth factor receptor)
PDGFRA 1417 platelet-derived growth factor receptor, alpha polypeptide
PDGFRB 1684 platelet-derived growth factor receptor, beta polypeptide
EGFR 8416 epidermal growth factor receptor
IGF1R 3244 insulin-like growth factor 1 receptor
NOTCH1 5272 notch 1
TGFBR1 1727 transforming growth factor, beta receptor 1
ADAM17 2014 ADAM metallopeptidase domain 17
-secretase 1470 Gamma-secretase complex cleaving NOTCH1receptor for signaling
Identification of polypharmacologic compounds for hepatocellular
carcinoma using Reaxys Medicinal Chemistry (RMC)
Figure 3: Screenshot of Reaxys Medicinal Chemistry search interface showing query for all
targets names in Table 1
Search for 18 targets from Table 1 in RMC yielded 461,151 bioactivities for 174,851 compounds. In
order to select most potent inhibitors that have high-affinity towards proteins in “Hepatocellular
Carcinoma Overview”, we filtered only compounds with pX>6. The filtering yielded 83,076 bioactivities
for 42,632 compounds. In order to find drugs that passed the first phase of clinical trials, i.e. compounds
that were found safe for humans we filtered only for compounds with highest clinical phase: II, III,
marketed, discontinued on II, discontinued on III. This filtering criterion yielded 2,232 bioactivities for
210 substances.
5. Figure 4: Reaxys Screenshot showing progressive filtering for polypharmacologic
compounds repositioned for hepatocellular carcinoma
Found bioactivities were exported into Microsoft Excel file using Export menu command in Reaxys with
“Hit data only” menu option. Using functions in Excel we further selected 515 unique compound-target
bioactivities with best pX. The complete list of 515 bioactivities is shown in Appendix A.
Identification of compounds tested against Hepatocellular Carcinoma using Pathway Studio
We were able to find 122 out of 215 RMC compounds in Pathway Studio by importing either Reaxys ID
or compounds names using Import->Import Entity list menu option in Pathway Studio. To find which
compounds were already tested against Hepatocellular Carcinoma we copied all compounds into new
pathway together with “Hepatocellular Carcinoma” disease entity. We then selected “Hepatocellular
Carcinoma” disease entity and used Add->Relations between Selected and Unselected menu option to
find compounds shown to inhibit “Hepatocellular Carcinoma” in various disease models or in patients.
Figure 5: 122 RMC compounds from Table 2 found in Pathway Studio, 52 of them have links
to “Hepatocellular Carcinoma” disease entity.
6. Table 2: List of unique polypharmacologic compounds from RMC found for “Hepatocellular
Carcinoma Overview” and their relations to Hepatocellular Carcinoma in Pathway Studio.
MedScan ID – compound identifier in Pathway Studio database; Reaxys ID - compound identifier in
Reaxys; # Targets – number of targets inhibited by compound in “Hepatocellular Carcinoma Overview”;
Relation to Hepatocellular Carcinoma– type of relation between compound and Hepatocellular
Carcinoma disease entity in Pathway Studio; # References – number of references supporting relation
between compound and Hepatocellular Carcinoma in Pathway Studio;
Reaxys ID Compound MedScan ID Relation to Hepatocellular
Carcinoma
# References # Targets
6674025 staurosporine 1206148 Regulation 7 10
9161676 vandetanib 1175784 Regulation 3 9
11404252 cediranib 1287940 ClinicalTrial 2 9
11404252 cediranib 1287940 Regulation 2 9
11326138 AEE788 1023004 Regulation 2 9
…….
…….
…….
Table shows partial data. To obtain the complete list of all polypharmacologic compounds the
workflow has to repeated in Pathway Studio 12.0