Towards structural systems pharmacology to study complex diseases and personalized medicine

Abstract

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Figure 1. A network view of drug action.

Dark blue lines represent drug–target interactions. Green arrows are protein–protein interactions or biological reaction pathways. Yellow nodes represent genes affected by genetic variation. These variations will impact drug action by changing the information flow of drug–target interactions in the biological network, even when these genes are not themselves the direct drug targets.

Figure 2. A structure-enabled integrative framework to model drug action.

Given a set of inputs—a new or existing drug, known bioactive chemical space, the whole human proteome, an individual's genotypic data, and context-specific phenotypic data—it is possible, in principle, to construct a structure-enabled integrative model of drug action. Such a model comprises multiple integrated functional modules (rounded boxes) that span multiple levels of biological organization and can be used to infer drug-induced arrhythmia. Solid and open arrows indicate current workflows and missing links, respectively. Blue boxes represent two existing methods: multiscale ventricular electrophysiological modeling – and protein–protein interaction (PPI) network-based predictive modeling for the prediction of drug-induced arrhythmia represented as a pseudo electrocardiography (ECG). The other boxes represent functional modules that are critically important but have not been fully developed or incorporated into the modeling process.

Figure 3. Hierarchical cause-effect semantic modeling to understand and predict drug action across temporal and spatial scales by using diverse techniques and integrating multiple sequencing, molecular, and omics data.

Arrowed edges represent cause-effect relationships between biological entities: genetic variation, ligand (allosteric or orthosteric), drug target, conformational state of the drug target, biological pathway, molecular phenotypes from multiple omics data, integrated biological network, and organism phenotype (e.g., disease). The thickness of the arrow indicates the degree of probability. And the + and − signs represent positive (or activated) and negative (or inhibited) regulation, respectively. For example, an allosteric ligand may interact with target 1 to induce its active conformation that positively regulates pathway 1. The positively regulated pathway 1 can be derived from an observed molecular phenotype 1 (e.g., gene expression profile). A context-specific biological network can be inferred by integrating multiple molecular phenotypes and be used to understand and predict an organismal phenotype.

Figure 4. Reconstruction of genome-wide, high-resolution protein–chemical interaction networks.

(A) Distribution of existing drug targets, PDB structures, and homology models in the human genome. (B) A schema to reconstruct 3-D drug–target interaction networks by integrating chemical genomics, structural genomics, and functional genomics. Novel drug off-targets could be identified by using the drug–target interaction models from chemical genomics analysis, and followed by searching for entire human or pathogen structural genome. In addition to sequence and global structural comparison, ligand binding site comparison is a valuable method, as it can identify binding promiscuity across fold space –, –. After putative off-targets have been identified from structural genomics analysis, sophisticated molecular modeling techniques such as protein-ligand docking and Molecular Dynamics (MD) simulation can be applied to determine high-resolution interaction models and their binding affinity and conformational space. To correlate drug–target interactions with their physiological response, the conformational state of the drug–-target complex can be mapped to biological pathways, integrated networks, and physiological models. Several examples are shown in the figure. Semantic-based modeling is able to establish cause-effect from drug to target to pathways and, ultimately, to clinical outcomes . Biological pathway analysis will provide the mechanistic understanding of information flow caused by drug modulation . Critical components and interactions involved in drug modulation can be identified through integrated protein–protein interaction (PPI) network analysis . Here, blue and green nodes represent drug targets and genes with observable changes, respectively. The target inhibition or activation along with genetic perturbations can be simulated using reconstructed physiological models . In turn, the information from pathway and network analysis can be used to verify or falsify the drug–target interaction models and to constrain their conformational space.

Figure 5. A proposed pathway that modulates the abundance and activity of CYP3A.

LCMT1 is a potential off-target for antibiotics. The inhibition of LCMT1 will activate PXR, thereby increasing the activity of CYP3A.