Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

New approaches to targeting the actin cytoskeleton for chemotherapy

The actin cytoskeleton is indispensable for normal cellular function. In particular, several actin-based structures coordinate cellular motility, a process hijacked by tumor cells in order to facilitate their propagation to distant sites. The actin cytoskeleton, therefore, represents a point for chemotherapeutic intervention. The challenge in disrupting the actin cytoskeleton is in preserving actin-driven contraction of cardiac and skeletal muscle. By targeting actin-binding proteins with altered expression in malignancy, it may be possible to achieve tumor-specific toxicity. A number of actin-binding proteins act cooperatively and synergistically to regulate actin structures required for motility. The actin cytoskeleton is characterized by a significant degree of plasticity. Targeting specific actin-binding proteins for chemotherapy will only be successful if no other compensatory mechanisms exist.

Is anticancer drug development heading in the right direction?

The success of molecularly targeted agents, such as imatinib, has led to expectations of a new era in anticancer drug development, and to a greatly increased focus on targeting as a strategy. However, the number of successes to date is small, and recent results suggest that the success of imatinib, for instance, in treating chronic myelogenous leukemia and gastrointestinal stromal tumor may be the exception rather than the rule. Here, we argue that the search for new anticancer agents needs to continue on as many fronts as possible, and not be focused on one strategy alone.