Editorial [Hot Topic: In Silico Lead Identification and Optimization for Drug Discovery (Executive Guest Editor: Shuxing Zhang)]

Drug discovery and development is a time-consuming and resource-demanding process. Even with significant increases of the global R&D expenses, the pharmas' discovery engines have stalled. Therefore, any technologies that can improve the efficiency of the pipeline would be highly appreciated. Current state-of-the-art in silico techniques provide cost-effective and time-efficient approaches for lead discovery and optimization. These tools have been widely applied to the pipeline, from the very upstream of target identification and validation to the very downstream of ADMET (absorption, distribution, metabolism, elimination, and toxicity) and preclinical modeling. This special issue of Current Pharmaceutical Design, for which I am honored to be the Executive Guest Editor, focuses on utilizations of these in silico technologies in drug discovery and development. The authors have reviewed recent progress in lead identification and optimization, focusing primarily on the cutting-edge in silico approaches combined with in vitro and in vivo experimental validation. Additionally, this issue will bring significant contribution to the understanding of a variety of complex diseases (e.g., cancer) as well as those important therapeutic targets involved in these diseases. Therefore it is appropriate for multidisciplinary readers. Kinases are playing an important role in cancer development. Schnieders et al. focus on the mitogen activated protein (MAP) kinases due to their crucial functions in cellular signaling. The majority of currently known MAP kinase inhibitors target an ATP binding pocket that is highly conserved in the human kinome. Here the authors review the progress toward the development of non-ATP competitive inhibitors, and special emphasis is placed on computational methods. Topics include recent advances in X-ray crystallography theory that improve MAP kinase structures, the use of molecular dynamics to understand the conformational heterogeneity of the activation loop, and inhibitors discovered by virtual screening. The impact of polarizable force fields such as AMOEBA is also discussed. In contrast to the non-ATP competitive agents, Wei and co-workers review a new type of kinase inhibitors, resorcylic acid lactones (RALs), which constitute a group of polyketide natural products with a large macrocyclic ring fused to resorcylic acid. Despite their core scaffold distinct from current kinase inhibitors, RALs with a cis-enone moiety have shown irreversible yet selective inhibition on a subset of kinases. This review discusses their mechanism of action, synthetic strategies, and structure-activity relationships (SARs). It is anticipated that these RAL analogs will diversify the chemical space of kinase inhibitors and facilitate the development of new leads for cancer treatment. In addition to cancer, Acevedo and co-authors describe the application of free energy perturbation (FEP) theory coupled to molecular dynamics (MD) or Monte Carlo (MC) statistical mechanics to the study of anti-HIV agents. They describe the early and recent successes in the design of human immunodeficiency virus type 1 (HIV-1) protease and non-nucleoside reverse transcriptase inhibitors. Furthermore, their ongoing work of optimizing leads for small molecule inhibitors of cyclophilin A (CypA) is highlighted. Their FEP-guided optimization, experimental synthesis, and biological testing of lead compounds have demonstrated a dose-dependent inhibition of HIV-1 infection in two cell lines. While the above authors focus on one type of targets or methods for inhibitor discovery, Chen et al. provide a comprehensive review of using various structure-based drug design approaches for pharmaceutical development. The authors assess current progress and challenges in lead identification and optimization based on protein targets. The state-of-the-art techniques for protein modeling (e.g. active site prediction, target flexibility modeling, etc.), hit identification (e.g. docking, molecular dynamics, focused library design, etc.), and polypharmacology design are discussed. They also explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. While Chen et al. touch on targeting protein-protein interactions for inhibitor design, Bienstock specifically focuses on this topic. She presents the new challenges in the area and summarizes current computational and experimental methods available for protein-protein docking. This review also tabulates some examples of successful design of antagonists and small molecule inhibitors targeting protein-protein interactions. Several of these drugs are beginning to appear in the clinic. Along the same line, Morrow and Zhang put a spotlight on hot spot residue identification. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features and shown increasing predictive success. Here they give an overview of current in silico prediction techniques of hot spot residues with a case study on the TNF receptor-associated factor 6 (TRAF6) protein. The percentage of failures in late pharmaceutical development is increasing due to drug toxicities. In their review, Gleeson and colleagues discuss the challenges of building in silico models on toxicology endpoints and their practical use in decision making. Special focus is put on the data and methods used to generate in silico toxicology models. Their strengths and weaknesses are also discussed. They conclude that, while the in silico toxicology is a valuable tool for drug discovery, much still needs to be done to understand more about the biological mechanisms of toxicity and to generate more rapid in vitro models for compound screening. A successful, efficacious, and safe drug must have a balance of properties, including potency against intended targets, appropriate ADME properties, and acceptable safety profiles. To achieve this balance, Segall presents multi-parameter optimization (MPO) approaches to simultaneously optimize many factors. In particular, he illustrates how MPO can be applied to efficiently design and select high quality compounds. He also describes the range of methods that have been employed in drug discovery. Finally, Nussinov's laboratory provides a perspective for the orthosteric and allosteric drugs. The orthosteric drugs should bind strongly to the target, and this would allow high selectivity with a low dosage. By contrast, the binding of allosteric drugs to the protein surface perturbs the structure and this perturbation finally reaches the binding site. They provide examples from functional in vivo scenarios for both types of cases, and suggest how high potency can be achieved in allosteric drug development. This issue of Current Pharmaceutical Design includes a plethora of information across multiple disciplines. I wish to thank all the authors and co-authors for their commitments and outstanding work. Special thanks are also given to those anonymous reviewers who have contributed significantly by their constructive suggestions to the excellence of this issue.