Identifying compound efficacy targets in phenotypic drug discovery

Schirle, Markus; Jenkins, Jeremy L.

doi:10.1016/j.drudis.2015.08.001

Cited by 131 publications

(97 citation statements)

References 72 publications

(68 reference statements)

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“…Computational methods are becoming increasingly important as a means of identifying potential new targets of drugs, especially since they are increasing in accuracy due to the much greater volume of high-quality publicly available data and their cost-effectiveness compared to experimental technologies [100,109]. Historically, we can distinguish between ligand- and structure-based computational methods.…”

Section: Towards Fully Exploiting Polypharmacology In Precision Oncologymentioning

confidence: 99%

Polypharmacology in Precision Oncology: Current Applications and Future Prospects

Antolín

Workman²,

Mestres³

et al. 2017

CPD

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Over the past decade, a more comprehensive, large-scale approach to studying cancer genetics and biology has revealed the challenges of tumor heterogeneity, adaption, evolution and drug resistance, while systems-based pharmacology and chemical biology strategies have uncovered a much more complex interaction between drugs and the human proteome than was previously anticipated. In this mini-review we assess the progress and potential of drug polypharmacology in biomarker-driven precision oncology. Polypharmacology not only provides great opportunities for drug repurposing to exploit off-target effects in a new single-target indication but through simultaneous blockade of multiple targets or pathways offers exciting opportunities to slow, overcome or even prevent inherent or adaptive drug resistance. We highlight the many challenges associated with exploiting known or desired polypharmacology in drug design and development, and assess computational and experimental methods to uncover unknown polypharmacology. A comprehensive understanding of the intricate links between polypharmacology, efficacy and safety is urgently needed if we are to tackle the enduring challenge of cancer drug resistance and to fully exploit polypharmacology for the ultimate benefit of cancer patients.

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Section: Towards Fully Exploiting Polypharmacology In Precision Oncologymentioning

confidence: 99%

Polypharmacology in Precision Oncology: Current Applications and Future Prospects

Antolín

Workman²,

Mestres³

et al. 2017

CPD

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“…Numerous methods have been developed to analyze large-scale high-throughput screening or small-molecule profiling data to generate hypotheses regarding MoA for compounds discovered phenotypically (Dancik et al, 2014; Schirle and Jenkins, 2015). Such inferential methods are particularly useful, and in fact possibly necessary, when dealing with the large amount of data generated by cancer cell line (CCL) profiling.…”

Section: Computational Approaches To Triangulate On Small-molecule Moamentioning

confidence: 99%

The Power of Sophisticated Phenotypic Screening and Modern Mechanism-of-Action Methods

Wagner

Schreiber

2016

Cell Chemical Biology

101

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The enthusiasm for phenotypic screening as an approach for small-molecule discovery has increased dramatically over the last several years. The recent increase in phenotype-based discoveries is in part due to advancements in phenotypic readouts in improved disease models that recapitulate clinically relevant biology in cell culture. Of course, a major historical barrier to using phenotypic assays in chemical biology has been the challenge in determining the mechanism of action (MoA) for compounds of interest. With the combination of medically inspired phenotypic screening and the development of modern MoA methods, we can now start implementing this approach in chemical probe and drug discovery. In this Perspective, we highlight recent advances in phenotypic readouts and MoA determination by discussing several case studies in which both activities were required for understanding the chemical biology involved and, in some cases, advancing toward clinical development.

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“…The general strategy for drug-target interaction predictions has been presented in several previous reviews [61,62]. Recent advances in data science, chemical genomics, and polypharmacology provide new opportunities for improving the performance of computational drug-target interaction predictions.…”

Section: Case Studies Of Data Science Applications To Drug Discoverymentioning

confidence: 99%

Providing data science support for systems pharmacology and its implications to drug discovery

Hart

Xie

2016

Expert Opinion on Drug Discovery

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Introduction The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. Areas covered This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Expert opinion Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

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Identifying compound efficacy targets in phenotypic drug discovery

Cited by 131 publications

References 72 publications

Polypharmacology in Precision Oncology: Current Applications and Future Prospects

Polypharmacology in Precision Oncology: Current Applications and Future Prospects

The Power of Sophisticated Phenotypic Screening and Modern Mechanism-of-Action Methods

Providing data science support for systems pharmacology and its implications to drug discovery

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