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

Abstract

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.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

“…An over-reliance on in vitro high-throughput drug screening (HTS)* and the “one-drug-one-target-one-disease” concept is cited by some as a contributing factor in the abundance of late-stage R&D failures in recent years, many of which were the result of poor efficacy and unexpected toxicity of lead compounds developed using HTS technology. 38,39 In contrast, certain experimental systems identify candidate drugs based on higher-level readouts of pharmacologic activity, in order to predict the effects of a compound in vivo. 40,41 Phenotypic screens using animal or cell-based models of disease offer improved performance in this regard, but come with their own set of drawbacks, including relatively low throughput, high expense, mechanistic uncertainty, and limited coverage of the full spectrum of human disease.…”

Harnessing Big Data for Systems Pharmacology

“…Moreover, a model alone may not be sufficient for a real-world application; the model is often dependent on multiple data sets. Big data integration is an important topic in systems pharmacology that has been covered elsewhere (9, 10). Beyond the data challenge, models are coupled strongly with algorithms underlying the model, software that implements the algorithm to execute the model, and tools that process inputs and outputs.…”

“…Machine learning–based big data analytics is playing an increasingly important role in systems pharmacology (10). Owing to the nature of pharmacological data that are often biased, incomplete, and heterogeneous, and our limited knowledge of biological systems and human physiology, the machine leaning models generalized using a specific algorithm and one particular set of data may not be applicable to a new case.…”

“…Recent developments in high-throughput experiments have generated a huge amount of data across the multiple biological scales of the organism, across a wide range of timescales, and across multiple species. These data provide unprecedented opportunities for systems pharmacology but impose great challenges in data processing, management, sharing, and integration (9, 10). The rapid advances in cloud computing, big data technology, and data science offer an opportunity to clear the hurdles in data-driven modeling for systems pharmacology.…”

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

“…Multiple target binding, i.e., polypharmacology, is a common phenomenon1. To understand how polypharmacology leads to the alteration of the cellular state through gene regulation, signaling transduction, and metabolism, and ultimately causes the change of the physiological or pathological state of the individual, a multi-scale modeling approach is needed23. In the framework of multi-scale modeling, drug targets are first predicted on a genome scale.…”