The implications of target saturation for the use of drug–target residence time

The translational value of ligand-receptor binding kinetics in drug discovery

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50/IC50 and KD/Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug-target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug-target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much-needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.

Surface Plasmon Resonance Biosensors: A Review of Molecular Imaging with High Spatial Resolution

Surface plasmon resonance (SPR) is a powerful tool for determining molecular interactions quantitatively. SPR imaging (SPRi) further improves the throughput of SPR technology and provides the spatially resolved capability for observing the molecular interaction dynamics in detail. SPRi is becoming more and more popular in biological and chemical sensing and imaging. However, SPRi suffers from low spatial resolution due to the imperfect optical components and delocalized features of propagating surface plasmonic waves along the surface. Diverse kinds of approaches have been developed to improve the spatial resolution of SPRi, which have enormously impelled the development of the methodology and further extended its possible applications. In this minireview, we introduce the mechanisms for building a high-spatial-resolution SPRi system and present its experimental schemes from prism-coupled SPRi and SPR microscopy (SPRM) to surface plasmonic scattering microscopy (SPSM); summarize its exciting applications, including molecular interaction analysis, molecular imaging and profiling, tracking of single entities, and analysis of single cells; and discuss its challenges in recent decade as well as the promising future.

Bioresponsive Polymers for Nanomedicine-Expectations and Reality!

Bioresponsive polymers in nanomedicine have been widely perceived to selectively activate the therapeutic function of nanomedicine at diseased or pathological sites, while sparing their healthy counterparts. This idea can be described as an advanced version of Paul Ehrlich's magic bullet concept. From that perspective, the inherent anomalies or malfunction of the pathological sites are generally targeted to allow the selective activation or sensory function of nanomedicine. Nonetheless, while the primary goals and expectations in developing bioresponsive polymers are to elicit exclusive selectivity of therapeutic action at diseased sites, this remains difficult to achieve in practice. Numerous research efforts have been undertaken, and are ongoing, to tackle this fine-tuning. This review provides a brief introduction to key stimuli with biological relevance commonly featured in the design of bioresponsive polymers, which serves as a platform for critical discussion, and identifies the gap between expectations and current reality.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Nanomedicine for brain cancer

CNS tumors remain among the deadliest forms of cancer, resisting conventional and new treatment approaches, with mortality rates staying practically unchanged over the past 30 years. One of the primary hurdles for treating these cancers is delivering drugs to the brain tumor site in therapeutic concentration, evading the blood-brain (tumor) barrier (BBB/BBTB). Supramolecular nanomedicines (NMs) are increasingly demonstrating noteworthy prospects for addressing these challenges utilizing their unique characteristics, such as improving the bioavailability of the payloadsviacontrolled pharmacokinetics and pharmacodynamics, BBB/BBTB crossing functions, superior distribution in the brain tumor site, and tumor-specific drug activation profiles. Here, we review NM-based brain tumor targeting approaches to demonstrate their applicability and translation potential from different perspectives. To this end, we provide a general overview of brain tumor and their treatments, the incidence of the BBB and BBTB, and their role on NM targeting, as well as the potential of NMs for promoting superior therapeutic effects. Additionally, we discuss critical issues of NMs and their clinical trials, aiming to bolster the potential clinical applications of NMs in treating these life-threatening diseases.

Chance Favors the Perplexed Mind: The Critical Role of Mechanistic Biochemistry in Drug Discovery

Scientific discoveries often start with an observation that does not quite make sense, within the framework of a well-established hypothesis. It is when researchers delve deeply to understand such perplexing data that established hypotheses are modified or replaced, and new and expanded knowledge of the system can be gained. This is often the case in the field of drug discovery. In this Perspective, case studies demonstrate how an understanding of perplexing data can lead to novel discoveries regarding the biological function of drug targets, or the mechanisms of compound-target interactions, that can ultimately result in new drugs entering the clinic. These case studies reinforce two interdependent themes: (1) that understanding the pathophysiological context in which drug targets function and the mechanistic details of drug-target interactions are critical to efficient and effective drug discovery and (2) that investing time and energy into following up on perplexing data can lead to novel discoveries that can drive the development of new and improved medicines.

Evolution of the drug-target residence time model

IntroductionThe pharmacological action of a drug is linked to its affinity for a specific molecular target as quantified by in vitro equilibrium measurements. However, it is clear that for many highly effective drugs, interactions with their molecular targets do not conform to simple, equilibrium conditions in vivo and this results in a temporal discordance between pharmacokinetics and pharmacodynamics. The drug-target residence time model was developed to provide a theoretical framework with which to understand cases in which very slow dissociation of the drug-target complex in vivo results in durable PD effects even after systemic concentrations of drug have waned.Area coveredIn this article, the author provides a brief description of the drug-target residence time model and focuses on the refinements that have been made to the original model to incorporate the influences of compound rebinding in cells and pharmacokinetic properties of drug molecules.Expert opinionThere is now overwhelming evidence for the utility of the drug-target residence time model as a framework for understanding in vivo drug action. The in vitro measured residence time (τ_R) must be used in concert with equilibrium measures of drug-target affinity (e.g. IC₅₀) and with in vivo measures of pharmacokinetic half-life, to afford the researcher a powerful approach to compound optimization for clinical effect. Despite the significant use and refinement of this model, continued studies are required to better understand the dynamic interplay between residence time, target pathobiology, drug distribution and drug pharmacokinetics.

Structure-Based Design of A-1293102, a Potent and Selective BCL-XL Inhibitor

BCL-XL, an antiapoptotic member of the BCL-2 family of proteins, drives tumor survival and maintenance and thus represents a key target for cancer treatment. Herein we report the rational design of a novel series of selective BCL-XL inhibitors exemplified by A-1293102. This molecule contains structural elements of selective BCL-XL inhibitor A-1155463 and the dual BCL-XL/BCL-2 inhibitors ABT-737 and navitoclax, while representing a distinct pharmacophore as assessed by an objective cheminformatic evaluation. A-1293102 exhibited picomolar binding affinity to BCL-XL and both efficiently and selectively killed BCL-XL-dependent tumor cells. X-ray crystallographic analysis demonstrated a key hydrogen bonding network in the P2 binding pocket of BCL-XL, while the bent-back moiety achieved efficient occupancy of the P4 pocket in a manner similar to that of navitoclax. A-1293102 represents one of the few distinct structural series of selective BCL-XL inhibitors, and thus serves as a useful tool for biological studies as well as a lead compound for further optimization.

Pharmacokinetic Principles and Their Application to Central Nervous System Tumors

Despite increasing knowledge of the biologic drivers of central nervous system tumors, most targeted agents trialed to date have not shown activity against these tumors in clinical trials. To effectively treat central nervous system tumors, an active drug must achieve and maintain an effective exposure at the tumor site for a long enough period of time to exert its intended effect. However, this is difficult to assess and achieve due to the constraints of drug delivery to the central nervous system. To address this complex problem, an understanding of pharmacokinetic principles is necessary. Pharmacokinetics is classically described as the quantitative study of drug absorption, distribution, metabolism, and elimination. The innate chemical properties of a drug, its administration (dose, route and schedule), and host factors all influence these four key pharmacokinetic phases. The central nervous system adds a level of complexity to standard plasma pharmacokinetics as it is a coupled drug compartment. This review will discuss special considerations of pharmacokinetics in the context of therapeutic development for central nervous system tumors.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.

Preclinical Models of Alzheimer's Disease: Relevance and Translational Validity

The only drugs currently approved for the treatment of Alzheimer's Disease (AD) are four acetylcholinesterase inhibitors and the NMDA antagonist memantine. Apart from these drugs, which have minimal to no clinical benefit, the 40-year search for effective therapeutics to treat AD has resulted in a clinical failure rate of 100% not only for compounds that prevent brain amyloid deposition or remove existing amyloid plaques but also those acting by a variety of other putative disease-associated mechanisms. This indicates that the preclinical data generated from current AD targets to support the selection, optimization, and translation of new chemical entities (NCEs) and biologics to clinical trials is seriously compromised. While many of these failures reflect flawed hypotheses or a lack of adequate characterization of the preclinical pharmacodynamic and pharmacokinetic (PD/PK) properties of lead NCEs-including their bioavailability and toxicity-the conceptualization, validation, and interrogation of the current animal models of AD represent key limitations. The overwhelming majority of these AD models are transgenic, based on aspects of the amyloid hypothesis and the genetics of the familial form of the disease. As a result, these generally lack construct and predictive validity for the sporadic form of the human disease. The 170 or so transgenic models, perhaps the largest number ever focused on a single disease, use rodents, mainly mice, and in addition to amyloid also address aspects of tau causality with more complex multigene models including other presumed causative factors together with amyloid. This overview discusses the current animal models of AD in the context of both the controversies surrounding the causative role of amyloid in the disease and the need to develop validated models of cognitive function/dysfunction that more appropriately reflect the phenotype(s) of human aged-related dementias. © 2019 by John Wiley & Sons, Inc.