Novel opportunities for computational biology and sociology in drug discovery

Abstract

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies.

Figure 1. Role of computational technologies in the drug discovery pipeline

This figure summarizes how computational biology can impact drug discovery. The various stages of the drug discovery pipeline (See textbox 1 for detailed background on each step) are listed in the left column. We note that the traditional linear process is shifting to become more parallel, simultaneous and cyclical: Red arrows indicate the traditional pipeline while yellow dashed arrows suggest novel workflows that are increasingly adopted by pharmaceutical and biotechnological companies to increase productivity. Biomarkers and analysis of of target molecules’ tissue distribution are the most recently introduced checkpoints and are not required by FDA. Computational biology methods discussed in the main text* are listed along the top row. Blue lines illustrate how each method is related with others. For example, sequence analysis relies on pattern recognition and classification; Text mining, terminologies and knowledge engineering are entwined, as are pattern recognition and classification. The impact of each computational technique on each stage of drug discovery (dependency between a pair of disciplines) is classified into three categories: actively or heavily used (big black dot), less actively used (small black dot) and our suggestion (small grey dot). *We do not emphasize chemical informatics in the main text because it relates to issues from chemistry and not biology. Chemical informatics comprises a wide range of approaches from computational and combinatorial chemistry that model lead properties and their interaction with targets. These include chemical structure and property prediction; structure activity relationships; molecular similarity and diversity analysis; compound classification and selection; chemical data collection, analysis and management; virtual drug screening; and the prediction of in vivo compound characteristics. PK: pharmacokinetics; PD: pharmcodynamics; ADME: absorption, distribution, metabolism, and excretion.

Figure 2. A provisional ontology for drug discovery

The ontology features a concept for drugs or small chemical molecules and another for drug synthesis and formulation. Further, a drug is known to target a specific molecule: protein, RNA, or DNA. A drug’s target is biologically active in the context of a pathway, cellular component, cell, and tissue. The drug is typically tested using an animal model for a specific human phenotype, usually a disease. A patient or human with the disease phenotype has a set of symptoms/signs, some of which can be unrelated to the disease. Drugs treat patients, but can cause adverse reactions. Finally, we link the patient’s drug response to the genetic variation in her genome and environmental factors encountered. These concepts are linked with directed or undirected relations, such as encodes (DNA encodes RNA), interacts with (drug-drug interactions), is part of (a cell is part of a tissue), affects (environmental factor affects genetic variation), and several others. We illustrate our ontology with the drug amantadine, which acts as a prophylactic agent against several RNA virus-induced influenzas that afflict the respiratory system and result in coughing and sneezing. Amantadine can cause a skin rash as a side effect.

Figure 3. Overview of promising computational opportunities in drug discovery

Text-mining enables the extraction of information from publications and clinical records. Mathematical modeling helps to assess experimental data in context of previously collected facts, while computational data integration distills multiple raw data types into a collection of computable biological statements. The resulting network of semantic relations can serve as a scaffold for modeling biological processes, design and optimization of therapeutic drug cocktails, and linking complex phenotypes to genotypes. The figure incorporates ontological concepts outlined in figure 2: cellular process (such as tissue necrosis), symptoms (in this case, sneezing, allergic rash), genetic variation (depicted as a single nucleotide polymorphism), and drugs (amantadine, valium, and aspirin, listed here top to down.