Smoothed Potential MD Simulations for Dissociation Kinetics of Etoposide To Unravel Isoform Specificity in Targeting Human Topoisomerase II

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Personalized medicine of non-gene-specific chemotherapies for non-small cell lung cancer

Non-small cell lung cancer is recognized as the deadliest cancer across the globe. In some areas, it is more common in women than even breast and cervical cancer. Its rise, vaulted by smoking habits and increasing air pollution, has garnered much attention and resource in the medical field. The first lung cancer treatments were developed more than half a century ago. Unfortunately, many of the earlier chemotherapies often did more harm than good, especially when they were used to treat genetically unsuitable patients. With the introduction of personalized medicine, physicians are increasingly aware of when, how, and in whom, to use certain anti-cancer agents. Drugs such as tyrosine kinase inhibitors, anaplastic lymphoma kinase inhibitors, and monoclonal antibodies possess limited utility because they target specific oncogenic mutations, but other drugs that target mechanisms universal to all cancers do not. In this review, we discuss many of these non-oncogene-targeting anti-cancer agents including DNA replication inhibitors (i.e., alkylating agents and topoisomerase inhibitors) and cytoskeletal function inhibitors to highlight their application in the setting of personalized medicine as well as their limitations and resistance factors.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Novel, Potent, and Druglike Tetrahydroquinazoline Inhibitor That Is Highly Selective for Human Topoisomerase II α over β

We disclose a novel class of 6-amino-tetrahydroquinazoline derivatives that inhibit human topoisomerase II (topoII), a validated target of anticancer drugs. In contrast to topoII-targeted drugs currently in clinical use, these compounds do not act as topoII poisons that enhance enzyme-mediated DNA cleavage, a mechanism that is linked to the development of secondary leukemias. Instead, these tetrahydroquinazolines block the topoII function with no evidence of DNA intercalation. We identified a potent lead compound [compound 14 (ARN-21934) IC₅₀ = 2 μM for inhibition of DNA relaxation, as compared to an IC₅₀ = 120 μM for the anticancer drug etoposide] with excellent metabolic stability and solubility. This new compound also shows ~100-fold selectivity for topoIIα over topoβ, a broad antiproliferative activity toward cultured human cancer cells, a favorable in vivo pharmacokinetic profile, and the ability to penetrate the blood–brain barrier. Thus, ARN-21934 is a highly promising lead for the development of novel and potentially safer topoII-targeted anticancer drugs.

Design, Synthesis, Dynamic Docking, Biochemical Characterization, and in Vivo Pharmacokinetics Studies of Novel Topoisomerase II Poisons with Promising Antiproliferative Activity

We previously reported a first set of hybrid topoisomerase II (topoII) poisons whose chemical core merges key pharmacophoric elements of etoposide and merbarone, which are two well-known topoII blockers. Here, we report on the expansion of this hybrid molecular scaffold and present 16 more hybrid derivatives that have been designed, synthesized, and characterized for their ability to block topoII and for their overall drug-like profile. Some of these compounds act as topoII poison and exhibit good solubility, metabolic (microsomal) stability, and promising cytotoxicity in three cancer cell lines (DU145, HeLa, A549). Compound 3f (ARN24139) is the most promising drug-like candidate, with a good pharmacokinetics profile in vivo. Our results indicate that this hybrid new chemical class of topoII poisons deserves further exploration and that 3f is a favorable lead candidate as a topoII poison, meriting future studies to test its efficacy in in vivo tumor models.