Target Based Drug Combination Selection
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Target Based Drug Combination Selection

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The presentation outlines an algorithm to identify combinations of drug for a given therapeutic endpoint....

The presentation outlines an algorithm to identify combinations of drug for a given therapeutic endpoint.

The objective is to target different stages of the disease pathway.

Chemoinformatics as employed by Ontomine, a US patent pending algorithm is employed for the same.

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Target Based Drug Combination Selection Target Based Drug Combination Selection Document Transcript

  • Target Based Drug Combination Selection Systems Biology Worldwide Combination Drug
    • Combination therapy with two or more agents having complementary mechanisms of action represents a type of incremental innovation that has extended the range of therapeutic options in the treatment of almost every human disease.
    • Combination products also known as fixed-dose combinations are combinations of two or more active drugs produced in single tablet.
    • The idea of combining two or more drugs with complementary mode of action is to produce additivity of desired therapeutic effect but not of the side effects.
    • Advantages of Combination Drugs
    • Simplifying Drug administration (fewer prescription, more compliance..)‏
    • Increases therapeutic potency.
    • Offers way to extend patent life by combining existing drugs.
    • Creating new market for combination which performs better than single-drug formulations
    • Combination drugs are often useful in multi-factorial diseases, chronic diseases and difficult to treat things like TB.
    • Combination drugs may help in reducing resistance evolution of 1 particular drug and issues associated with SNP's.
    Methods to find potential Drug Combinations Searching Drug Combination Space Searching potential drugs among set of drugs (N) (binary/ternary combination) requires factorial design to consider all possible combinations ( N C 2 or N C 3 ), however selection process can be optimized by using search algorithms. We have used statistic derived from various parallel analysis (Chemoinformatics analysis by Ontomine, Docking, Gene Expression Network Analysis...) as a deciding criteria to search potential drug/combination. Hypothesis
    • We considered following criteria while ranking drug combination:
    • Target proteins of constituent drug in combination should be related with disease of interest.
    • There should be minimum overlap between target-protein profile among drug constituent, to avoid competitive binding.
    • Constituent drug should be specific i.e. it should target protein(s) related with disease of interest and should not target unrelated protein (to avoid toxicities/side-effect).
    • Drugs targeting proteins which occupy critical position in expression network (Hubs) are relatively more potential.
    Analysis flow
    • Our analytical method could be summarized under following steps:
    • Target Identification
      • Reverse Docking
      • Ontomine
      • Gene Expression Network Analysis
    • Target Selection
      • Data integration
      • Forward Docking
    • Combination Search
      • Computation of statistic
      • Drug and/or drug-combination ranking and selection.
    Ontomine Analysis Gene Network Analysis Network analysis can reveal many important information about disease mechanism, and it's been used to select target genes for disease. Network can be generated based on gene/protein expression data. Network analysis algorithm focuses on finding small networks, which are easier to interpret and validate. It computes p-values for sub-networks, which helps us in identifying significant subnetworks. This analysis produced list of significant subnetworks. * Network is generated by borrowing information from protein-protein interaction, pathways database. * Network could be generated from Gene expression and/or Protein expression data. Algorithm Comments
    • Steps:
    • Compute Int_score for all drugs used in analysis.
    • Rank drug (Higher Int_score implies higher potency).
    • Compute average/median Int_score (M1).
    • Drugs with int_score greater then M1 are selected as seed for binary analysis.
    • Compute Int_score for binary combination, restrict search space to drug-combination starting with drugs selected in step 4. Repeat step 2 to 3, Select pairs with Int_score>= Avg. Int_score for binary combination.
    • Compute Int_score for triple combination, restrict search space to drug-combination starting with binary combinations selected in step 5. Repeat step 2 to 3, Select triples with Int_score>= Avg. Int_score for triple combination.
    We have applied novel idea for selection of Drug Combination by integration of ideas from different fields like Chemoinformatics, Structural Informatics, Bioinformatics, and Microarray Data Analysis. Our analytical method was successful in detecting combination drug available in market.
    • Selection of best possible combinations from set of drugs is generally done through trial and error methods, however analytical methods can be applied to logically select drug combinations.
    • Drug combination can be selected by following methods:
    • Physicochemical parameters like dose-response readings, which involves analyzing dose-response curve of individual drug and extrapolating behaviour for drug combination Such method provides statistic for different combination. Does not provide explanation about mechanism of action for drug-combination.
    • Target Profiling : Careful study of target protein(s) for drug/drug combination can provide many important details like, its mode of action, adverse behaviour because of non-specific binding, efficacy of drug, target overlap between constituent drugs. This method provides biological explanation behind efficacy of drug combination
    Docking Reverse Docking : Potential binding proteins may be discovered by docking a drug to repository of drug target database. Potential Drug Target Database (PDTD) is used for reverse docking. The objective of this method is to find drug targets. Forward Docking: Docking is performed on few target proteins selected by analysis. The objective of this method is to estimate predicted Ki and understand binding behaviour of Drug. Ontomine transforms the structural information for chemically, biologically or pharmacologically related molecules to a hierarchical schema of functional groups. It discovers patterns in the related schema and predicts biological activity, Toxicity & Side-effect using rules inferred from analyzing the patterns. Target profiling in Ontomine is done by mining Drug Target and BioAssay KnowledgeBase for set of Drugs. Output of Ontomine analysis is set of target protein for particular drug along with confidence level of prediction. Ontomine is also used to generate ADME-Tox profile of drug. Int_score : It is a summary statistic which combines result of various analysis (Ontomine/Docking/Network Analysis). It also considers target specificity, competitive binding, ADME-Tox profile for a given drug or combination Target proteins identified by Ontomine, Docking and Network Analysis are mapped on KEGG disease specific pathways, followed by enrichment analysis for mechanistic understanding of drug and/or drug combination action. Systems Biology Worldwide SBW © A B