This document discusses principles of rational drug combination design and personalized therapy based on network pharmacology. It provides several examples:
1) Using gene expression signatures to identify drug combinations that improve drug sensitivity, such as dexamethasone and sirolimus for acute lymphoblastic leukemia.
2) Designing combinations based on synthetic lethal screens, such as identifying genes that sensitize cancer cells to epidermal growth factor receptor inhibitors.
3) A strategy for personalized cancer therapy based on identifying genes with synthetic lethal interactions with oncogenes like KRAS, and using these genes as therapy targets depending on a patient's mutation status.
4) A concept called "synergistic outcome determination" to model
Rational drug design is a process that begins with knowledge of a biological target and aims to design small molecules that interact optimally with that target to produce a desired therapeutic effect. It involves analyzing the structures of active molecules and known targets, then designing new molecules that are predicted to specifically fit the target. This may involve modifying existing lead compounds or building new ones de novo. The goal is to develop drugs with greater potency, selectivity and fewer side effects than those found by traditional trial-and-error means. Cimetidine for reducing stomach acid is provided as an example of rational drug design, where histamine analogs were synthesized and optimized until an effective and safe product was obtained.
This document discusses concepts and approaches in drug design. It describes how drug design involves developing analogues and prodrugs through chemical modifications to a lead molecule. Analogues can be synthesized by changing substitution groups or carbon skeletal structure. Prodrugs are active metabolites formed from parent compounds through biotransformation. Lead discovery involves exploring new molecules and exploiting leads through assessment and extension. Random and nonrandom screening are used to identify potential leads. Pharmacokinetic and pharmacodynamic studies of metabolites can also lead to new leads. Drug design approaches include molecular hybridization, conjunction, and disjunction of structural elements as well as rational approaches considering physicochemical properties and electronic features.
In this slide I presented the Computer Aided Drug Design and its type :
1.Structure based Drug Design
2. Ligand based Drug Design and its Applications.
A drug is defined as any substance that causes a physiological change in the body when introduced through various routes of administration. Drug design is the process of discovering new drugs based on knowledge of a biological target. De novo drug design is a continuous process that uses the 3D structure of a receptor to design new molecules that can bind to and modulate the target. It involves determining the structures of lead targets and complexes and using molecular modeling tools to modify lead compounds. Various computational methods exist for de novo drug design, including growing, linking, lattice-based sampling and molecular dynamics-based approaches.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
Molecular descriptors are numerical values that characterize molecular properties and structures. They can represent physicochemical properties or values derived from algorithmic techniques applied to molecular structures. Descriptors vary in complexity and computational requirements. Some are based on experimental data while others are algorithmic constructs. Two-dimensional (2D) descriptors are calculated from 2D structures and include counts, physicochemical properties, and topological indices. Three-dimensional (3D) descriptors encode spatial relationships and include fragment screens and pharmacophore keys.
Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Str...Pavan Kumar
This document describes research conducted to identify potential inhibitors of the FtsZ protein in Mycobacterium tuberculosis as candidates for new anti-tuberculosis drugs. 2D and 3D quantitative structure-activity relationship (QSAR) models were developed based on a dataset of benzimidazole compounds. Pharmacophore modeling identified common features among active compounds. Molecular docking was used to analyze compound binding to the FtsZ protein. Virtual screening identified novel hit compounds for further evaluation. The study aims to provide insights into important structural properties of FtsZ inhibitors to guide future drug design and development efforts against tuberculosis.
Rational drug design is a process that begins with knowledge of a biological target and aims to design small molecules that interact optimally with that target to produce a desired therapeutic effect. It involves analyzing the structures of active molecules and known targets, then designing new molecules that are predicted to specifically fit the target. This may involve modifying existing lead compounds or building new ones de novo. The goal is to develop drugs with greater potency, selectivity and fewer side effects than those found by traditional trial-and-error means. Cimetidine for reducing stomach acid is provided as an example of rational drug design, where histamine analogs were synthesized and optimized until an effective and safe product was obtained.
This document discusses concepts and approaches in drug design. It describes how drug design involves developing analogues and prodrugs through chemical modifications to a lead molecule. Analogues can be synthesized by changing substitution groups or carbon skeletal structure. Prodrugs are active metabolites formed from parent compounds through biotransformation. Lead discovery involves exploring new molecules and exploiting leads through assessment and extension. Random and nonrandom screening are used to identify potential leads. Pharmacokinetic and pharmacodynamic studies of metabolites can also lead to new leads. Drug design approaches include molecular hybridization, conjunction, and disjunction of structural elements as well as rational approaches considering physicochemical properties and electronic features.
In this slide I presented the Computer Aided Drug Design and its type :
1.Structure based Drug Design
2. Ligand based Drug Design and its Applications.
A drug is defined as any substance that causes a physiological change in the body when introduced through various routes of administration. Drug design is the process of discovering new drugs based on knowledge of a biological target. De novo drug design is a continuous process that uses the 3D structure of a receptor to design new molecules that can bind to and modulate the target. It involves determining the structures of lead targets and complexes and using molecular modeling tools to modify lead compounds. Various computational methods exist for de novo drug design, including growing, linking, lattice-based sampling and molecular dynamics-based approaches.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
Molecular descriptors are numerical values that characterize molecular properties and structures. They can represent physicochemical properties or values derived from algorithmic techniques applied to molecular structures. Descriptors vary in complexity and computational requirements. Some are based on experimental data while others are algorithmic constructs. Two-dimensional (2D) descriptors are calculated from 2D structures and include counts, physicochemical properties, and topological indices. Three-dimensional (3D) descriptors encode spatial relationships and include fragment screens and pharmacophore keys.
Exploration of a potential FtsZ inhibitors as new scaffolds by Ligand and Str...Pavan Kumar
This document describes research conducted to identify potential inhibitors of the FtsZ protein in Mycobacterium tuberculosis as candidates for new anti-tuberculosis drugs. 2D and 3D quantitative structure-activity relationship (QSAR) models were developed based on a dataset of benzimidazole compounds. Pharmacophore modeling identified common features among active compounds. Molecular docking was used to analyze compound binding to the FtsZ protein. Virtual screening identified novel hit compounds for further evaluation. The study aims to provide insights into important structural properties of FtsZ inhibitors to guide future drug design and development efforts against tuberculosis.
This lecture discusses computer aided drug design (CADD). CADD uses computational methods to aid in drug discovery and design. It involves identifying drug targets, generating potential drug molecule "hits" through virtual screening, and optimizing "leads" through modeling protein-ligand interactions and simulating the effects of molecular modifications. The key steps are target identification, validation of targets, lead identification through screening libraries or de novo design, lead optimization through docking simulations, and preclinical/clinical trials. CADD approaches like ligand-based, target-based, and structure-based drug design can speed up the drug development process and reduce costs compared to traditional experimental methods alone.
1) The document discusses the basics of drug design including defining the disease process, identifying targets for drug design like enzymes, receptors and nucleic acids, and the different approaches of ligand-based drug design and structure-based drug design.
2) It also covers important techniques in drug design like computer-aided drug design using computational methods, quantitative structure-activity relationships (QSAR), and the uses of computer graphics in molecular modeling and dynamics simulations.
3) Important experimental techniques discussed are x-ray crystallography and NMR spectroscopy that provide structural information for target biomolecules essential for structure-based drug design.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
This document discusses structure based drug design. It describes how drug design uses knowledge of biological targets to find new medications. Structure based drug design uses information about the 3D structure of protein targets to design ligands that bind to them. The main methods described are ligand-based drug design through database searching, and receptor-based drug design which builds ligands for a receptor. Molecular docking is also discussed as a key technique to predict how ligands bind to protein targets and identify potential drug candidates.
Rational drug design involves identifying a biological target related to a disease, determining the target's structure and function, and designing drug molecules that interact with the target in a beneficial way. Key aspects of rational drug design include using computational tools to model protein targets based on their 3D structure, designing drugs that complement the target's active site, and generating new drug leads through database searching and de novo design methods. The goal is to develop effective medications in a time and cost efficient manner by applying knowledge of a drug target's molecular properties.
This document discusses rational drug design, which involves designing drugs based on knowledge of biological targets. It describes two main approaches: structure-based drug design, which relies on determining the 3D structure of the target using techniques like X-ray crystallography, and ligand-based drug design, which relies on knowledge of molecules that already bind to the target. Structure-based design involves identifying a drug target, determining its structure and function, then designing drugs that interact with it beneficially. Homology modeling can be used to model targets when experimental structures are unavailable. The document outlines the steps of structure-based design in rational drug development.
The document discusses lead identification and optimization in drug design. It describes the general drug discovery process which includes target validation, assay development, high-throughput screening, hit to lead identification, and lead optimization stages. Lead optimization is one of the most important steps and involves modifying lead compounds to improve potency, selectivity, and pharmacokinetic parameters. Structure-based and ligand-based drug design approaches are used, along with in silico tools to predict properties like toxicity and ensure drug-likeness. Key steps in structure-based design include identifying the binding site and growing fragments in an iterative process until an optimized lead is obtained.
This document provides an overview of pharmacophore mapping and pharmacophore-based screening. It defines a pharmacophore as the pattern of molecular features responsible for a drug's biological activity. The key steps in pharmacophore modeling are identifying common binding elements in active compounds, generating potential ligand conformations, and determining the 3D relationships between pharmacophore elements. Pharmacophore models can be generated manually based on known active ligands or automatically using software. Receptor-based pharmacophore generation uses the 3D structure of the target protein to identify favorable binding sites. Overall, pharmacophore mapping is used in computer-aided drug design to identify novel ligands that interact with the same biological target.
This document discusses the process of drug design and development, beginning with identifying lead compounds that can bind to protein receptors and modify their function. It then outlines the steps of target validation, high-throughput screening, lead optimization, preclinical and clinical drug development. Specific techniques discussed include structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) to help modify lead compounds. The document also briefly covers pharmacokinetics, the formulation of an HIV-1 protease inhibitor, and its mechanism of binding to the active site of the protease enzyme to lower viral levels.
Lecture 10 pharmacophore modeling and sar paradoxRAJAN ROLTA
The document discusses pharmacophore modeling, which involves identifying the 3D arrangement of functional groups necessary for a molecule to bind to a target site and trigger a biological response. It notes that pharmacophore modeling is important for understanding receptor-ligand interactions and for drug design. Two types are described: ligand-based, which extracts common chemical features from known ligands in the absence of the target structure, and structure-based, which generates features from the target or target-ligand complex structure. Pharmacophore models can be used for virtual screening to identify molecules that encode the required interaction pattern. The document also discusses the structure-activity relationship and notes that similar molecules do not always have similar activities, known as the SAR paradox.
Computer aided drug design uses computational methods to facilitate the design and discovery of new therapeutic solutions. There are two main types of drug design - ligand-based which relies on knowledge of molecules that bind to the target, and structure-based which relies on the 3D structure of the target. The main steps in structure-based design are target selection, binding site identification, molecular docking to predict how ligands bind to the target, and scoring to evaluate interactions. Computational tools are used for databases, molecular modeling, docking, screening, and predicting absorption and toxicity properties. These tools help speed up the drug design process and make it more efficient.
Some building blocks for Rational Drug Design samthamby79
The document discusses various approaches to drug design and discovery, including general screening, serendipity, and rational drug design. It describes rational drug design as beginning with knowledge of chemical responses in the human body to create treatment profiles. Computational methods like structure-based design are used to identify novel compounds, design safe drugs, and develop clinical candidates. Proteomics and genomics are also discussed as they relate to drug targets and development.
Dr. Kumbhare Manoj R. discusses enzyme inhibition in drug discovery. There are two main approaches to drug discovery - target-based and physiology-based. For the past 20 years, the target-based approach of developing drugs that affect a specific target has been dominant. Enzymes are excellent targets for drug development due to their essential roles and the suitability of their active sites for inhibitor interactions. Many top-selling drugs are enzyme inhibitors that work through reversible or irreversible inhibition mechanisms. The development of ACE inhibitors to control hypertension is provided as an example of a successful clinical application of enzyme inhibition.
Pharmacophore identification and novel drug designBenittabenny
pharmacophore is a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes. This identification leads to the development of designing a new drug.
This document discusses ligand-based drug design approaches. It defines ligand-based drug design as relying on knowledge of other molecules that bind to the biological target to derive a pharmacophore model or quantitative structure-activity relationship (QSAR) model. The most important method is pharmacophore modeling, which develops a model of interactions between ligands and the target receptor from the ligand perspective. Key ligand-based design approaches covered are pharmacophore modeling, QSAR, scaffold hoping, and pseudo receptors.
Computer aided drug design uses computational approaches to aid in the drug discovery process. There are several key approaches including ligand based approaches which identify characteristics of known active ligands, target based approaches which use information about the biological target, and structure based drug design which utilizes 3D structural information. The main steps in drug design include target identification and validation, lead identification and optimization, and preclinical and clinical trials. Computational tools are used throughout the process for tasks like molecular docking, ADMET prediction, and structure activity relationship analysis.
This document discusses computational aided drug design. It begins by defining drug and the drug design process. It describes that the selected drug molecule should be an organic small molecule that is complementary in shape and oppositely charged to the target biomolecule. It then discusses ligand based and structure based drug design approaches. Various techniques used in drug design are also summarized such as x-ray crystallography, NMR, homology modeling, and computer aided drug design. Benefits of computational aided drug design include streamlining drug discovery, eliminating compounds with undesirable properties, and identifying and optimizing new drugs in a time and cost effective manner.
This document discusses the synthesis of analogue circuits to realize a given network function. It begins by reviewing the properties and characteristics required for network functions to correspond to realizable passive circuits. It then examines four canonical forms - Foster and Cauer forms - for realizing LC, RC, and RL network functions as either impedance or admittance driving point functions through partial fraction or continued fraction expansions. Specific examples are worked through to illustrate the realization of LC network functions using the different forms. Finally, it notes the properties of RC driving point functions before discussing their realization.
SynergyScreen is an R package that I have developed to facilitate design and analysis of medium-throughput compound synergy screens using single-ray design. These slides describe the functionality of the package and R programming techniques. These slides were presented at Madison R Meet-up (MadR) on 18 January 2017.
This lecture discusses computer aided drug design (CADD). CADD uses computational methods to aid in drug discovery and design. It involves identifying drug targets, generating potential drug molecule "hits" through virtual screening, and optimizing "leads" through modeling protein-ligand interactions and simulating the effects of molecular modifications. The key steps are target identification, validation of targets, lead identification through screening libraries or de novo design, lead optimization through docking simulations, and preclinical/clinical trials. CADD approaches like ligand-based, target-based, and structure-based drug design can speed up the drug development process and reduce costs compared to traditional experimental methods alone.
1) The document discusses the basics of drug design including defining the disease process, identifying targets for drug design like enzymes, receptors and nucleic acids, and the different approaches of ligand-based drug design and structure-based drug design.
2) It also covers important techniques in drug design like computer-aided drug design using computational methods, quantitative structure-activity relationships (QSAR), and the uses of computer graphics in molecular modeling and dynamics simulations.
3) Important experimental techniques discussed are x-ray crystallography and NMR spectroscopy that provide structural information for target biomolecules essential for structure-based drug design.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
This document discusses structure based drug design. It describes how drug design uses knowledge of biological targets to find new medications. Structure based drug design uses information about the 3D structure of protein targets to design ligands that bind to them. The main methods described are ligand-based drug design through database searching, and receptor-based drug design which builds ligands for a receptor. Molecular docking is also discussed as a key technique to predict how ligands bind to protein targets and identify potential drug candidates.
Rational drug design involves identifying a biological target related to a disease, determining the target's structure and function, and designing drug molecules that interact with the target in a beneficial way. Key aspects of rational drug design include using computational tools to model protein targets based on their 3D structure, designing drugs that complement the target's active site, and generating new drug leads through database searching and de novo design methods. The goal is to develop effective medications in a time and cost efficient manner by applying knowledge of a drug target's molecular properties.
This document discusses rational drug design, which involves designing drugs based on knowledge of biological targets. It describes two main approaches: structure-based drug design, which relies on determining the 3D structure of the target using techniques like X-ray crystallography, and ligand-based drug design, which relies on knowledge of molecules that already bind to the target. Structure-based design involves identifying a drug target, determining its structure and function, then designing drugs that interact with it beneficially. Homology modeling can be used to model targets when experimental structures are unavailable. The document outlines the steps of structure-based design in rational drug development.
The document discusses lead identification and optimization in drug design. It describes the general drug discovery process which includes target validation, assay development, high-throughput screening, hit to lead identification, and lead optimization stages. Lead optimization is one of the most important steps and involves modifying lead compounds to improve potency, selectivity, and pharmacokinetic parameters. Structure-based and ligand-based drug design approaches are used, along with in silico tools to predict properties like toxicity and ensure drug-likeness. Key steps in structure-based design include identifying the binding site and growing fragments in an iterative process until an optimized lead is obtained.
This document provides an overview of pharmacophore mapping and pharmacophore-based screening. It defines a pharmacophore as the pattern of molecular features responsible for a drug's biological activity. The key steps in pharmacophore modeling are identifying common binding elements in active compounds, generating potential ligand conformations, and determining the 3D relationships between pharmacophore elements. Pharmacophore models can be generated manually based on known active ligands or automatically using software. Receptor-based pharmacophore generation uses the 3D structure of the target protein to identify favorable binding sites. Overall, pharmacophore mapping is used in computer-aided drug design to identify novel ligands that interact with the same biological target.
This document discusses the process of drug design and development, beginning with identifying lead compounds that can bind to protein receptors and modify their function. It then outlines the steps of target validation, high-throughput screening, lead optimization, preclinical and clinical drug development. Specific techniques discussed include structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs) to help modify lead compounds. The document also briefly covers pharmacokinetics, the formulation of an HIV-1 protease inhibitor, and its mechanism of binding to the active site of the protease enzyme to lower viral levels.
Lecture 10 pharmacophore modeling and sar paradoxRAJAN ROLTA
The document discusses pharmacophore modeling, which involves identifying the 3D arrangement of functional groups necessary for a molecule to bind to a target site and trigger a biological response. It notes that pharmacophore modeling is important for understanding receptor-ligand interactions and for drug design. Two types are described: ligand-based, which extracts common chemical features from known ligands in the absence of the target structure, and structure-based, which generates features from the target or target-ligand complex structure. Pharmacophore models can be used for virtual screening to identify molecules that encode the required interaction pattern. The document also discusses the structure-activity relationship and notes that similar molecules do not always have similar activities, known as the SAR paradox.
Computer aided drug design uses computational methods to facilitate the design and discovery of new therapeutic solutions. There are two main types of drug design - ligand-based which relies on knowledge of molecules that bind to the target, and structure-based which relies on the 3D structure of the target. The main steps in structure-based design are target selection, binding site identification, molecular docking to predict how ligands bind to the target, and scoring to evaluate interactions. Computational tools are used for databases, molecular modeling, docking, screening, and predicting absorption and toxicity properties. These tools help speed up the drug design process and make it more efficient.
Some building blocks for Rational Drug Design samthamby79
The document discusses various approaches to drug design and discovery, including general screening, serendipity, and rational drug design. It describes rational drug design as beginning with knowledge of chemical responses in the human body to create treatment profiles. Computational methods like structure-based design are used to identify novel compounds, design safe drugs, and develop clinical candidates. Proteomics and genomics are also discussed as they relate to drug targets and development.
Dr. Kumbhare Manoj R. discusses enzyme inhibition in drug discovery. There are two main approaches to drug discovery - target-based and physiology-based. For the past 20 years, the target-based approach of developing drugs that affect a specific target has been dominant. Enzymes are excellent targets for drug development due to their essential roles and the suitability of their active sites for inhibitor interactions. Many top-selling drugs are enzyme inhibitors that work through reversible or irreversible inhibition mechanisms. The development of ACE inhibitors to control hypertension is provided as an example of a successful clinical application of enzyme inhibition.
Pharmacophore identification and novel drug designBenittabenny
pharmacophore is a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes. This identification leads to the development of designing a new drug.
This document discusses ligand-based drug design approaches. It defines ligand-based drug design as relying on knowledge of other molecules that bind to the biological target to derive a pharmacophore model or quantitative structure-activity relationship (QSAR) model. The most important method is pharmacophore modeling, which develops a model of interactions between ligands and the target receptor from the ligand perspective. Key ligand-based design approaches covered are pharmacophore modeling, QSAR, scaffold hoping, and pseudo receptors.
Computer aided drug design uses computational approaches to aid in the drug discovery process. There are several key approaches including ligand based approaches which identify characteristics of known active ligands, target based approaches which use information about the biological target, and structure based drug design which utilizes 3D structural information. The main steps in drug design include target identification and validation, lead identification and optimization, and preclinical and clinical trials. Computational tools are used throughout the process for tasks like molecular docking, ADMET prediction, and structure activity relationship analysis.
This document discusses computational aided drug design. It begins by defining drug and the drug design process. It describes that the selected drug molecule should be an organic small molecule that is complementary in shape and oppositely charged to the target biomolecule. It then discusses ligand based and structure based drug design approaches. Various techniques used in drug design are also summarized such as x-ray crystallography, NMR, homology modeling, and computer aided drug design. Benefits of computational aided drug design include streamlining drug discovery, eliminating compounds with undesirable properties, and identifying and optimizing new drugs in a time and cost effective manner.
This document discusses the synthesis of analogue circuits to realize a given network function. It begins by reviewing the properties and characteristics required for network functions to correspond to realizable passive circuits. It then examines four canonical forms - Foster and Cauer forms - for realizing LC, RC, and RL network functions as either impedance or admittance driving point functions through partial fraction or continued fraction expansions. Specific examples are worked through to illustrate the realization of LC network functions using the different forms. Finally, it notes the properties of RC driving point functions before discussing their realization.
SynergyScreen is an R package that I have developed to facilitate design and analysis of medium-throughput compound synergy screens using single-ray design. These slides describe the functionality of the package and R programming techniques. These slides were presented at Madison R Meet-up (MadR) on 18 January 2017.
Data analysis and Visualisation Techniques for Compound Combination ModellingRichard Lewis
A talk given at the EBI for the Cambridge Cheminformatics Network Meeting on the 25/11/2015, introducing compound combination modelling analysis and visualisation techniques.
Data Visualisation Assignment - using TablueAditya Dashora
The document discusses weighted data points in a Nielsen survey. It analyzed customer confidence index scores from different countries, which varied in participant numbers. The analysis weighted each country's data by its percentage of the total confidence index. Several maps then show interest levels across various countries for deciding purchases of certain products and services using social media. These products/services include automobiles, apps, beauty products, clothing, entertainment, financial products, food, home electronics, toys, travel, and restaurants. A final chart compares country-wide interest in various online shopping-related activities.
This document discusses drug interactions and fixed dose combinations. It classifies drug interactions as additive, synergistic, antagonistic, or functional. Pharmacokinetic interactions can occur during absorption, distribution, metabolism, or excretion. Pharmacodynamic interactions act on receptors or body systems. Fixed dose combinations provide convenience but the component drugs must have matching pharmacokinetic profiles and doses should be based on their volumes of distribution and concentrations. Overall benefits include improved compliance but individual dose titration is not possible.
Commonly used Statistics in Medical Research HandoutPat Barlow
We found this handout to be incredibly useful as a guide and resource for non-statistical professionals to make quick decisions about statistical methods. The handout accompanies the Commonly Used Statistics in Medical Research Part I Presentation
Fixed dose combination products – rationality, status in india, development i...Dr Sukanta sen
The development of FDCs is becoming increasingly
important from a public health perspective.
•They are being used in the treatment of a wide range of
conditions and are particularly useful in the management of
human immunodeficiency virus/acquired immunodeficiency
syndrome (HIV/AIDS), malaria and tuberculosis, which are
considered to be the foremost infectious disease threats in the world today.
Hypertension remains difficult to treat effectively despite available drugs. Aggressive treatment of moderate and mild hypertension through combinations of drugs from different classes leads to better outcomes. Combination therapy is recommended to control blood pressure as it is more effective than monotherapy due to targeting multiple mechanisms. Fixed-dose combinations have advantages over individual drugs such as better blood pressure control, fewer side effects, and increased compliance.
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Hypertension affects a large portion of the global population and is a major risk factor for cardiovascular disease. While treatment has improved, blood pressure remains difficult to control for many patients. Guidelines recommend treating with multiple-drug combinations to effectively lower blood pressure and reduce cardiovascular risk. Fixed-dose combinations can simplify treatment regimens and improve patient compliance compared to free-drug combinations, helping to better control blood pressure and reduce health risks.
This document discusses fixed drug dose combinations (FDC). It notes that FDC involves combining two drugs in a single formulation. The drugs should have similar half-lives and dosage ratios based on pharmacokinetics. An example given is amoxicillin and clavulanic acid. Advantages of FDC include convenience, enhanced effects, and reduced side effects. However, disadvantages include inability to adjust doses independently and increased adverse effects in some cases. Rational FDCs improve compliance and efficacy while irrational FDCs can cause harm.
This document defines and provides formulas for several statistical analysis methods: frequency and percentage distribution to calculate percentages for data profiles; mean to calculate the average value; t-test to determine if there are significant differences between the means of two variables; analysis of variance (ANOVA) to determine if frequencies differ significantly among multiple groups; Pearson product-moment correlation coefficient to measure the association between two variables; multiple correlation to test the relationship between independent and dependent variables; and multiple regression to predict dependent variables from independent variables.
Common statistical tools used in research and their usesNorhac Kali
Descriptive statistics are used to summarize and describe data through measures like means and percentages. They aim to describe a sample rather than make inferences about the underlying population. Parametric statistics assume the data comes from a known probability distribution and allow inferences about the distribution's parameters, but require the data to meet certain assumptions. Non-parametric methods make fewer assumptions and allow comparisons of ordinal data, making them more robust and widely applicable than parametric methods.
1. Dose response relationships can be represented by either graded or quantal curves, with graded curves showing a continuous response to varying doses and quantal curves showing the proportion of subjects responding at different doses.
2. Key features of dose response curves include the median effective dose (ED50) which produces a 50% response, potency which is measured by the dose required for 50% effect, and the therapeutic index which is the ratio of toxic to effective doses.
3. Both curve types provide information about a drug's potency but graded curves also indicate maximum efficacy while quantal curves show variability in individual responses.
Commonly Used Statistics in Survey ResearchPat Barlow
This is a version of our "commonly used statistics" presentation that has been modified to address the commonly used statistics in survey research and analysis. It is intended to give an *overview* of the various uses of these tests as they apply to survey research questions rather than the point-and-click calculations involved in running the statistics.
This document discusses using hierarchical clustering algorithms to mine pharmacogenomic data from a clinical data warehouse containing both genomic and clinical trial data. It describes hierarchical clustering as an approach to analyze gene expression microarray data by grouping single profiles into clusters in a tree structure. Six main hierarchical clustering algorithms are outlined that differ in how they calculate distances between clusters. The goal of pharmacogenomic data mining is to discover knowledge that can identify the most effective and least toxic drugs for individuals based on their genetic makeup and disease.
This document describes the development of gene expression signatures that can predict sensitivity to various chemotherapeutic drugs using microarray data from cancer cell lines.
1) Signatures were developed for several drugs including docetaxel, topotecan, adriamycin, etoposide, 5-fluorouracil, paclitaxel, and cyclophosphamide that could accurately predict drug sensitivity in independent cancer cell line datasets.
2) These signatures were also shown to predict clinical response to the drugs in human patients, including predicting response to docetaxel in breast cancer and ovarian cancer with over 85% accuracy.
3) The signatures were specific to each individual drug and could predict response to multid
Instructions Respond to your colleague in one of the following TatianaMajor22
Instructions:
Respond to your colleague in one of the following ways:
· If your colleagues’ posts influenced your understanding of these concepts, be sure to share how and why. Include additional insights you gained.
· If you think your colleagues might have misunderstood these concepts, offer your alternative perspective and be sure to provide an explanation for them. Include resources to support your perspective.
**minimum of three (3) scholarly references are required for each reply cited within the body of the reply & at the end**
Reply # 1
Sheila Shafer
Initial Post
Top of Form
Constitutive receptor activity/inverse agonism and functional selectivity/biased agonism are two concepts in contemporary pharmacology possessing significant indications for drug use in medicine and research and new drug development processes since a drug can be simultaneously an agonist, an antagonist, and an inverse agonist acting at the same receptor (Berg & Clarke, 2018). Agonists are drugs with affinity (bind to the target receptor) and intrinsic efficacy (change receptor activity to produce a response). Antagonists have affinity but zero intrinsic efficacy; therefore, they bind to the target receptor but do not produce a response. Occupying a fraction of the receptor population (defined by the affinity of the antagonist), an antagonist reduces the probability of occupancy by an agonist. Thus, the presence of an antagonist will reduce receptor occupancy by an agonist with a corresponding reduction in response. Dopamine receptor antagonism is a compelling molecular target for treating various psychiatric disorders, including substance use disorders (Wager et al., 2017).
G-protein-coupled receptors (GPCRs) or seven transmembrane receptors (7-TM receptors) are the most prominent family of human cell surface receptors, a large protein group positioned on the cell membrane which binds to extracellular elements (Rehman & Dimri, 2021). GPCRs then transmit signals from extracellular substances to the G protein in the intracellular space. Ion gated channels control access in and out of neurons. The voltage-gated ion channels (VGIC) permit only one type of ion to permeate; ligand-gated ion channels (LGIC) are less selective, enabling two or more types of ions to pass through the channel pore (Clar & Maani, 2021). The agonist spectrum is a comparison that relates to both ion channels and G-protein-linked receptors. Both g couple proteins and ion gated channels are protein receptors embedded in cell membranes that bind to a molecule.
Epigenetics refers to the genetic information directed at defining the role of the entire genome. It studies heritable and stable changes in gene expression occurring through modifications in the chromosome rather than in the DNA sequence. Epigenetic mechanisms regulate gene expression through chemical changes of DNA bases and alterations to the chromosomal superstructure (Al Aboud et al., 2021). Phar ...
Reconstruction and analysis of cancerspecific Gene regulatory networks from G...ijbbjournal
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Pharmacogenomics is the study of how genes affect individual responses to drugs. It combines pharmacology and genomics to develop safe and effective personalized medications and dosages based on a person's genetic makeup. The goal is to improve treatment outcomes by predicting drug effectiveness and reducing adverse reactions. Challenges include implementing genetic tests in clinical practice and addressing cost, ethical and legal issues. Future applications include developing tailored drugs for many diseases and faster, more targeted clinical trials through biomarkers.
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This document describes a bioinformatic methodology to predict synthetic lethal drug targets for cancers deficient in the tumor suppressor gene E-cadherin (CDH1). The methodology analyzes gene expression data from public databases to identify genes whose expression levels correlate with CDH1. Known synthetic lethal interactions, like between BRCA and PARP1, were correctly predicted. Several candidate synthetic lethal partners of CDH1 were identified and grouped into biological pathways. This bioinformatic approach can efficiently predict synthetic lethal targets to guide experimental validation and help develop targeted therapies for CDH1-deficient cancers.
Genomics is the study of all nucleotide sequences in an organism, including genes, noncoding regions, and regulatory elements. Determining the human genome sequence involved forming DNA libraries, sequencing clones, and ordering sequences using computational analysis. Genomics has applications in medicine like gene testing for inherited disorders and understanding drug responses. Pharmacogenomics studies how genetic differences impact individual drug metabolism and effects, aiming to prevent adverse drug reactions by screening for risk alleles.
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Nc state lecture v2 Computational ToxicologySean Ekins
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This document discusses computer assisted drug discovery (CADD) in plant pathology. It begins by outlining problems faced by plant protectionists like pathogen variability and pesticide resistance that necessitate new targeted approaches. The key stages of CADD are then described, including identifying suitable drug targets in plant pathogens, generating 3D structures through homology modeling or crystallography, molecular docking to screen compounds, and ligand-based approaches like pharmacophore modeling and QSAR when no target structure is available. Case studies applying these CADD methods to discover treatments for various fungal and bacterial diseases are also mentioned. The document concludes by noting potential challenges in applying CADD for plant pathogens.
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.
Cardiotoxicity is unfortunately a common side effect of many modern chemotherapeutic agents. The mechanisms that underlie these detrimental effects on heart muscle, however, remain unclear. The Drug Toxicity Signature Generation Center at ISMMS aims to address this unresolved issue by providing a bridge between molecular changes in cells and the prediction of pathophysiological effects. I will discuss ongoing work in which we use next-generation sequencing to quantify changes in gene expression that occur in cardiac myocytes after they are treated with potentially toxic chemotherapeutic agents. I will focus in particular on the computational pipeline we are developing that integrates sophisticated sequence alignment, statistical and network analysis, and dynamical mathematical models to develop novel predictions about the mechanisms underlying drug-induced cardiotoxicity.
Jaehee Shim is a Ph.D candidate in the Biophysics and Systems Pharmacology Program at Icahn School of Medicine at Mount Sinai (ISMMS). As a part of her Ph.D. studies, she is building dynamical prediction models based on analysis of gene expression data generated by the Drug Toxicity Signature Generation Center at ISMMS. She received her B.S in Biochemistry from the University of Michigan-Dearborn. Prior to starting her Ph.D, Jaehee worked at the ISMMS Genomics Core with a team of senior scientists and gained experience in improving and troubleshooting RNA sequencing protocols using Next Generation Sequencing Platforms.
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This document discusses challenges in designing pharmacogenomics clinical trials. It provides an overview of pharmacogenomics and different types of pharmacogenomics studies. It then discusses three common clinical trial designs - subgroups analysis design, enrichment design, and genotype-guided design - and their advantages and disadvantages. Key challenges in pharmacogenomics clinical trials include small sample sizes for subgroups, possible confounding and selection biases, and statistical power issues. Prospective clinical trials are needed to validate predictive biomarkers and assess clinical utility of genotype-guided treatments.
Similar to The Principle of Rational Design of Drug Combination and Personalized Therapy Based on Network Pharmacology (20)
2. 326 J. Xiong et al.
dependency or joint dependency between genes and disease phenotype, is the key
“battle map” for rational drug combinations and design of personalized therapy.
We also tentatively outline several aspects of the process which might help drive
innovation in network construction and shape the future development of network
pharmacology applications.
Keywords Network pharmacology • Rational drug design • Personalized medicine
• Synthetic lethality • Combination drug discovery
Abbreviations
EGFR Epidermal growth factor receptor
PARP Poly (ADP-ribose) phosphatase
ALL Acute lymphoblastic lymphoma
SOD Synergistic outcome determination
RNAi RNA interference
STAT3 Signal transducer and activator of transcription 3
RNA Ribonucleic acid
DNA Deoxyribonucleic acid
BRCA Breast cancer type 1 susceptibility protein
PLK1 Polo-like kinase 1
CDC16 Cell division cycle protein 16
STK33 Serine/threonine kinase 33
1 Introduction
Network Pharmacology attempts to model the effects of drug action by simultaneously
modulating multiple components in a gene network (Hopkins 2008; Yildirim et al.
2007). However, the theoretical basis for this new concept is still in its infancy and
the process by which we translate network modeling to clinical application remains
indirect (Hopkins 2008; Csermely et al. 2005; Kitano 2007). In this chapter, we try
to outline the principles of rational designs for drug combination and personalized
therapy based on network pharmacology by deciphering several milestone exam-
ples. We demonstrate that the network, which characterizes the dependency or joint
dependency between genes and disease phenotype, is the key “battle map” for rational
drug combinations and design of personalized therapy. We also tentatively outline
several aspects of the process which might help drive innovation in network con-
struction and shape the future development of network pharmacology applications.
A core task of a drug discovery study is to identify the dependency between the
genetic/molecular makeup of the human body and disease phenotype. Disease phe-
notype can be dependent on an individual causal gene, which means perturbations
3. 32714 The Principle of Rational Design of Drug Combination and Personalized…
acting on this gene might lead to a shift of the phenotype (from disease status to
normal status). In general, complex diseases are often dependent on many genes
rather than on a few genes, as has been demonstrated in the concept of “synthetic
lethal” (see below). Thus, it is also important to determine the co-dependency existing
between genes that drive the change in phenotype.
Synthetic lethal is one of most important concepts in current oncology drug
development and is a core research topic in network pharmacology studies (Hopkins
2008). Synthetic Lethality refers to a specific type of genetic interaction between
two genes, where mutation of one gene is viable but mutation of both leads to death
of cells (Kaelin 2005). The core of synthetic lethal concept is the joint dependency
or synergy between two genes in terms of cell fate. This concept can therefore, be
exploited to develop an effective therapeutic strategy. For example, by using an
inhibitor targeted to a Poly (ADP-Ribose) Polymerase (PARP) that is synthetically
lethal to a cancer-specific mutation (BRCA), researchers could target cancer cells to
achieve antitumor activity in tumors with the BRCA mutation (Fong et al. 2009).
According to a recent review, there are more than 21 compounds in clinical trials
that are based on a synthetic lethal approach, and there are at least 63 trials for
PARP inhibitors based on the synthetic lethal between PARP and BRCA (Shaheen
et al. 2011). Using PARP inhibitor for patients with BRCA gene mutation identified
via a genetic test of BRCA mutations is a typical use of a personalized therapy
strategy (Luo et al. 2009a). There are already several drug combinations, experi-
mentally validated, that clearly show sensitivity-improvement effects toward known
oncology drugs (Kim et al. 2011; Toledo et al. 2011; Whitehurst et al. 2007).
By extending this approach to the genome scale, a strategy based on screening syn-
thetic lethal relationships, then constructing a synthetic lethal gene network and
identifying multiple site perturbations, one can form a rational approach for drug
combination design.
2 Rational Drug Combination Design Based on Gene
Expression Pattern
One of the pioneering studies that used gene expression signatures to establish the
connections between small molecules, genes and disease was the “Connectivity
Map” project by Lamb et al. by which he illustrated the possibility of rational
drug combinations or personalized therapy design (Lamb et al. 2006). Taking
Dexamethasone for acute lymphoblastic leukemia (ALL) treatment as example,
they first generated the gene expression signatures associated with Dexamethasone
sensitivity/resistance, and then another drug, Sirolimus, which could revert dexam-
ethasone resistance (or “improve dexamethasone sensitivity”) was identified by
querying the perturbation to the gene expression pattern induced by small molecules.
The logical steps were as following:
Step 1: Having selected an initial drug D1 as a recognized treatment for the disease
of interest, the gene expression signature of drug sensitivity can be determined
4. 328 J. Xiong et al.
by comparing sensitive cell lines or patient cells against resistance cell lines (the
in vitro signature) or patient cells (the in vivo signature). An example of this is
illustrated in Fig. 14.1. In this figure, gene g1 is positively associated with drug
D1 sensitivity (Fig. 14.1a), whereas gene g2 negatively associated with drug D1
sensitivity (Fig. 14.1b).
Step 2: query the Connectivity Map with the D1 drug sensitivity signature, and
search for a candidate drug D2 which shows a positive correlation with drug
D1 sensitivity signature. As illustrated in Fig. 14.1c: if the treatment with drug D2
could up-regulate gene g1, and simultaneously down-regulate gene g2, then drug
D2 is a good candidate for improve drug D1 sensitivity (Fig. 14.1c). As a whole
treatment, the combined treatment with D1-D2 will show better sensitivity
than drug D1 alone (Fig. 14.1d). This method was actually “signature-based”
rather than “network-based”, because it used global gene expression profiling
as the space to search the optimal drug combination but did not explicitly model
the relationships between genes.
Drug D1
Drug D2
Drug D2Drug D1
Drug D1
Drug D1
Drug
Sensitivity
Drug
Sensitivity
Gene g1 Gene g2
a b
dc
Dexamethasone
+ Sirolimus
Dexamethasone
alone
dexamethasone concentation (μm)
0
0.001 0.01 0.1 1
20
40
60
80
100
120
viability(%ofcontrol)
Gene g1
Up
Regulation
Positive
Gene-Drug Sensitivity
Correlated Genes
Negative
Gene-Drug Sensitivity
Correlated Genes
Down
Regulation
Gene g2
Gene Expression Gene Expression
Positive
Gene-Drug Sensitivity
Correlation
Negative
Gene-Drug Sensitivity
Correlation
Drug
Sensitivity
Fig. 14.1 Rational drug combination design based on gene expression patterns. (a) Gene g1
is positively correlated with drug D1 sensitivity. (b) Gene g2 is negatively correlated with drug D1
sensitivity. (c) Query and search for a candidate drug D2 which show positive correlation with drug
D1 sensitivity signature (up-regulating g1, and down-regulating g2). (d) The sensitivity of drug D1
(Dexamethasone) and drug D2 (Sirolimus) combination (Adapted from Lamb et al. 2006)
5. 32914 The Principle of Rational Design of Drug Combination and Personalized…
3 Rational Drug Combination Design Based
on Synthetic Lethal
Synthetic lethal siRNA screens with chemical agents could facilitate to explore
the new determinants of sensitivity of known drugs, and identify new agents that
could selectively and synergistically enhance their therapeutic effects. Whitehurst
et al. combined a high-throughput cell-based genetic screening platform with a
genome-wide synthetic library of chemically synthesized small interfering RNAs
and established a paclitaxel-dependent synthetic lethal screen for identifying gene
targetsthatspecificallyreducedcellviabilityinthepresenceofpaclitaxel(Whitehurst
et al. 2007). The identified targets were enriched in proteasome subunit, microtu-
bule-related process and cell adhesion. Several of these targets sensitized lung cancer
cells to paclitaxel concentrations 1,000-fold lower than was otherwise required
for a significant response. Thus, this method demonstrates an effective approach to
design new drug combination: in this example, combining paclitaxel with the
identified small molecules interfered with the above biological processes which
were synthetic lethal to paclitaxel treatment. From these initial findings, a rational
drug treatment combination of proteasome inhibitor with paclitaxel could be designed.
Indeed, the collaboration of bortezomib, a proteasome inhibitor and paclitaxel has
already been clinically demonstrated (Davies et al. 2005).
Synthetic lethality could also be utilized to counteract drug resistance. Many tumors
fail to respond to therapy because of intrinsic or acquired resistance. To investigate
this possibility, Astsaturov et al. constructed an epidermal growth factor receptor
(EGFR)-centered signaling network by integrating multiple data sets, and then
conducted a targeted RNA interference screening (Astsaturov et al. 2010). In this
way, they identified subsets of genes that sensitize cells to EGFR inhibition. They
found that these sensitizing hits populate a protein network connected to EGFR,
which is in line with the concept that the gene sub-network closely linked to the
therapeutic target would be enriched for determinants of drug resistance. Erlotinib
is a reversible tyrosine kinase inhibitor, which acts on the EGFR. Chemical inhibi-
tion of proteins encoded by hit genes, e.g., the small-molecule inhibitor of STAT3
activation and dimerization, Stattic, could synergizes with erlotinib in reducing cell
viability and tumor growth (Astsaturov et al. 2010). In this way, synthetic lethality
screening provided a rational method to the design of combination cancer therapies
via counteracting drug resistance (Fig. 14.2).
4 Personalized Therapy Design Based on Synthetic Lethal
Recently, Luo et al. demonstrated a strategy to design personalized cancer therapy
based on synthetic lethal screening (Luo et al. 2009a). They first identified, via
a genome-wide RNAi screen, a group of genes which exhibited synthetic lethal
6. 330 J. Xiong et al.
interactions with the KRAS oncogene. The results highlighted a pathway involving
the mitotic kinase PLK1, the anaphase-promoting complex/cyclosome and the
proteasome that; when this pathway was inhibited, resulted in the death of Ras
mutant cells. Based on these findings and using the CDC16 gene as example, this
information could be used to design a personalized therapy as follows (see Fig. 14.3):
Step 1, analysis of the association of CDC16 gene expression with the prognosis
of cancer patients with the normal (wild type) Ras gene. As shown in Fig. 14.3a
(‘Ras signature-’), there are no significant differences in the survival curve of
CDC16 high expression patients (red line) and CDC16 low expression patients
(blue line), the log-rank test p-value is 0.67. It suggests that in Ras wild type
Fig. 14.2 The principle of design drug combination based on synthetic lethal
Fig. 14.3 The principle for design personalized therapy based on synthetic lethal (Adapted
from Figure 7 of Luo et al. 2009a)
7. 33114 The Principle of Rational Design of Drug Combination and Personalized…
patients, CDC16 gene expression is not associated with patient prognosis, and
it might be, therefore, ineffective to use this gene as therapy target in this group
of patients.
Step 2, analysis of the association of CDC16 gene expression with prognosis
of cancer patients with a Ras gene mutation. As shown in Fig. 14.3b (‘Ras signa-
ture+’), there are significant differences in the survival curve of CDC16 high
expression patients (red line) with CDC16 low expression patients (blue line),
here the log-rank test p-value is 0.02. This suggests that in Ras mutation patients,
CDC16 gene expression is significantly associated with patient prognosis, and the
therapy targeting to CDC16 has the potential to work in this group of patients.
Step 3, combining the above evidence, a hypothetic personalized therapeutic
strategy would be as follows: “if there is a therapy targeting CDC16, then it is
recommended that the patients will be tested for Ras gene mutation detection
before accepting this therapy. If the test result is positive (Ras mutation), then a
CDC16 targeted therapy is recommended. If the test result is negative, then the
patient is unlikely to benefit from this therapy”.
Similarly, Scholl et al. used high-throughput RNA interference (RNAi) to identify
synthetic lethal interactions in cancer cells harboring mutant KRAS, the most com-
monly mutated human oncogene. They identified the serine/threonine kinase STK33
as a target for treatment of mutant KRAS-driven cancers (Scholl et al. 2009).
However, there was a lack of structural abnormalities or deregulated expression of
STK33 in cancer cell lines and primary human cancer samples, which suggested
that STK33 does not act as a classical oncogene. Recent findings suggests that
cancers are not only dependent on mutated oncogenes, which drive the malignant
phenotype, but also dependent on some “normal” genes, which is termed “non-
oncogene addiction” (Solimini et al. 2007; Luo et al. 2009b). Thus a synthetic lethal
screen might be a practical approach to identify this type of association.
5 SOD – an In Vivo Genetic Interaction Similar
to Synthetic Lethality
Recently, our group proposed a novel in vivo genetic interaction which we call
‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic
Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer
patients’ outcome, whose correlation with outcome is due to cooperative, rather
than independent, contributions of genes (Xiong et al. 2010).
An illustration of this concept is in Fig. 14.4. Here the expression of two genes
(gene A, gene B) and their relationship between phenotype (patient prognosis) are
represented:
1. Gene A and gene B have two states: high expression or low expression levels.
2. Red triangles represent ‘bad outcome’ patients (shorter survival time or metastasis),
and green rectangles represent ‘good outcome’ patients (longer survival time
or non-metastasis).
8. 332 J. Xiong et al.
3. Individual gene expression is uncorrelated with patient outcome. For example,
given the gene A state is ‘low expression’, all patients with A (Low) are distrib-
uted in two clusters (50 % bad outcome and 50 % good outcome).
4. In combination, the expression states of two genes are sufficient to determine the
patient outcome. Given the combination of the states of A and B, i.e., A (Low) B
(high), 100 % patients are ‘good outcome’ (Fig. 14.4).
In this way, gene-gene pairs which synergistically determine patient outcome
could be identified by a “synergy” calculation based on information theory (Xiong
et al. 2010). Of interest, the concept of SOD has several unique features that differ
from those of the concept of Synthetic Lethality (Table 14.1):
1. In synthetic lethality, the phenotype is defined at the cell-level (i.e. cell death),
whereas SOD defines the phenotype at the physiological level (i.e. the survival
Gene A
Good Outcome
Bad Outcome
GeneB
LOW
LOW HIGH
HIGH
Fig. 14.4 The concept of ‘synergistic outcome determination’ (SOD) (Adapted from Xiong
et al. 2010)
Table 14.1 SOD vs synthetic lethality
Feature compared SOD Synthetic lethality
Phenotype Survival outcome of individual patient Cell death/growth
Systems level Tumor microenvironment (tissue level) Cell
Data accessible Human population (via computation) Yeast (SGA); Human cell lines
9. 33314 The Principle of Rational Design of Drug Combination and Personalized…
outcome of the individual). Thus, SOD provides a direct link between gene
level events and the clinical information.
2. Because of the ethical limitations, it is impossible to identify in vivo synthetic
lethal genes in human individuals, at present, high throughput synthetic lethality
screening is limited only to in vitro human cell lines (Whitehurst et al. 2007). But
in terms of SOD, it could be computationally inferred via combining the high-
throughput gene expression data with prognosis information from large human
populations.
3. Compared to using gene expression from in vitro cell lines in synthetic lethal
identification, we have used the gene expression information from a bulk of
tumor tissues when calculating SOD. Thus, it is possible to capture molecular
events at the tissue level rather than at the cellular level. This feature is important
to oncology studies, because the gene expression profiling data for a tumor tissue
is actually a representative of the information from a mixture of tissues which
include epithelial cells and other cells in the microenvironment. In this way, SOD
is useful for characterization of gene events in the tumor micro-environment.
6 Rational Drug Combination Design Based on SOD
Based on the SOD concept, a prognosis-guided synergistic gene-gene interaction
network could be constructed. Because this network characterizes the global joint
dependency between genes in a network manner, it is possible to design drug com-
binations based on the derived SOD network. As illustrated in our previous study, we
projected drug sensitivity-associated genes on to the cancer-specific SOD network, and
defined a perturbation index for each drug based upon its characteristic perturbation
pattern on network (Xiong et al. 2010). In this way, we demonstrated a strategy for
rational design of drug combinations. The steps and algorithms are as followings:
1. Given a cancer-specific SOD network, calculate the perturbation value of each
gene node by a specific drug. Here we can map the drug action to the gene network
by drug sensitivity-associated genes (Xiong et al. 2010). For example, the sensi-
tivity of primary drug (D1) is associated with four genes in Fig. 14.5a, thus we
label these genes as ‘1’ (Gene1, Gene 2, Gene3, Gene4) to represent the action
model of drug D1.
2. Calculate the perturbation value of each edge in the network for a particular drug.
If, and only if, both two nodes in an edge are labeled ‘1’, will the perturbation
value of this edge be labeled as ‘1’. For example, we can see drug D1 simultane-
ously perturbs Gene 1 and Gene 4 in Fig. 14.5a, thus the link between Gene 1 and
Gene 4 is labeled with ‘1’.
3. Calculate the perturbation index for each drugs according to:
=
=
=
∑
∑
1
1
PI
N
jj
M
ii
D
D
10. 334 J. Xiong et al.
Fig. 14.5 Rational drug combination design based on SOD
11. 33514 The Principle of Rational Design of Drug Combination and Personalized…
Here, the N is the number of edges, M is the number of genes in the network. Di
is
the perturbation value of gene node I and Dj
is the perturbation value of edge j.
(a) In the example illustrated in Fig. 14.5a, primary drug D1 perturbed 1 edge (link
from Gene 1 to Gene 4), and 4 nodes (Gene1, Gene 2, Gene3 and Gene4). Thus,
the perturbation index of primary drug D1 is 1/4=0.25;
(b) The action model of primary drug (D1) + candidate drug (D2) is illustrated in
Fig. 14.5b. Here the action of D2 added a perturbation to Gene 5, thus, this
changes the perturbation value of three edges into ‘1’ (the link from Gene 1 to
Gene 5, the link from Gene 2 to Gene 5, the link from Gene 3 to Gene 5) and
results in a perturbation index of D1+D2 is 4/5=0.8;
(c) For another candidate drug D3, the action model of primary drug (D1) + candidate
drug (D3) is illustrated in Fig. 14.5c. Here the action of D3 added a perturba-
tion to Gene 6, in this case this change the perturbation value of only one edge
into ‘1’ (the link from Gene 3 to Gene 6). Thus, the perturbation index of
D1+D3 is 2/4=0.5;
(d) Because the perturbation index of D1+D2 larger than that of D1+D3, the com-
bination D1-D2 is predicted to outperform the combination D1-D3.
In above case, the candidate drug D2 and D3 both perturbed one gene, but
resulted in significantly different perturbation indices. The reason for this is because
D2 perturbed gene 5, which exhibited more synergistic links with other genes (Gene
1, Gene 2 and Gene 3).
7 Conclusions and Perspective
From these examples, we have shown that the network characterizing the depen-
dency or joint dependency between genes and disease phenotype is the key “battle
map” for rational drug combination and personalized therapy design. In addition
to being able to determine synthetic lethal interactions via genetic screening,
computationally inferred in silico genetic interactions could also be utilized to
globally interrogate drug combination synergy. In theory, there are many potential
novel types of ‘genetic interaction’:
1. Genetic interactions could be defined by various types of phenotypes.
Traditionally, synthetic lethal is defined by the phenotype of cell viability
based on in vitro experiments. This type of information could be derived from model
organisms (e.g., yeast) or in vitro cultured human cell lines. A specific type of genetic
interaction is the interaction between drug and gene; for example, the sensitivity of
an oncology drug is dependent on individual genes that can be identified by chemical-
genetic screening (Muellner et al. 2011). Here, the phenotype is cell viability under
the two perturbed conditions (both drug treatment and RNAi interference for indi-
vidual genes) (Muellner et al. 2011).
2. Genetic interaction could be defined at different levels of “building blocks of life”.
12. 336 J. Xiong et al.
Since complex biology systems can be divided into various systems levels, so the
interactions between various systems levels (such gene level, gene module level,
protein complex, etc.) could also interrogated. For example, at the gene module
level, the combinatorial influence of deregulated gene modules on disease pheno-
type classification could be inferred by a synergy calculation (Park et al. 2010).
This interaction between gene modules could also computationally inferred and
applied to determination of drug combinations (Xiong et al. 2010). Beyond the
intra-cell events, the dependency between different cells could also contribute
to cancer phenotype and serve as potential targets for cancer treatment. It has
already been demonstrated that combinatorial therapy which targets inter-cell inter-
actions. i.e., interaction between cancer cells and stromal cells (Bronisz et al. 2011;
Aharinejad et al. 2009), as well as interaction between cancer stem cells and their
niche (Malanchi et al. 2012), could hold the potential to counteract the in vivo drug
resistance of cancer drugs.
Genetic interaction is a specific relationship within a triplet of gene-gene-phenotype
or gene-chemical-phenotype. Because it is possible to define a broad range of
phenotype at different levels within the human body, there are abundant opportuni-
ties to define new types of genetic interactions. Innovation in genetic interaction
definition and corresponding network construction holds great potential for applica-
tion to next generation oncology therapeutics.
Acknowledgements This work was partly supported by the grant from the Chinese Scientific and
Technological Major Special Project (2012ZX09301003-002-003), the National Natural Science
Foundation of China (91129708), the grant from State Key Lab of Space Medicine Fundamentals
and Application (SMFA) to J.X (SMFA09A07, SMFA10A03).
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