Expert Opinion on Drug Discovery最新文献

Introduction: Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

Areas covered: The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

Expert opinion: To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Introduction: The quest for novel MS therapies focuses on promoting remyelination and neuroprotection, necessitating innovative drug design paradigms and robust preclinical validation methods to ensure efficient clinical translation. The complexity of new drugs action mechanisms is strengthening the need for solid biological validation attempting to address all possible pitfalls and biases precluding access to efficient and safe drugs.

Areas covered: In this review, the authors describe the different in vitro and in vivo models that should be used to create an integrated approach for preclinical validation of novel drugs, including the evaluation of the action mechanism. This encompasses 2D, 3D in vitro models and animal models presented in such a way to define the appropriate use in a global process of drug screening and hit validation.

Expert opinion: None of the current available tests allow the concomitant evaluation of anti-inflammatory, immune regulators or remyelinating agents with sufficient reliability. Consequently, the collaborative efforts of academia, industry, and regulatory agencies are essential for establishing standardized protocols, validating novel methodologies, and translating preclinical findings into clinically meaningful outcomes.

Introduction: Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

Areas covered: This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

Expert opinion: Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Introduction: Abundant evidence suggests that the overexpression of CDK2-cyclin A/E complex disrupts normal cell cycle regulation, leading to uncontrolled proliferation of cancer cells. Thus, CDK2 has become a promising therapeutic target for cancer treatment. In recent years, insights into the structures of the CDK2 catalytic site and allosteric pockets have provided notable opportunities for developing more effective clinical candidates of CDK2 inhibitors.

Area covered: This article reviews the latest CDK2 inhibitors that have entered clinical trials and discusses the design and discovery of the most promising new preclinical CDK2 inhibitors in recent years. Additionally, it summarizes the development of allosteric CDK2 inhibitors and CDK2-targeting PROTACs. The review encompasses strategies for inhibitor and PROTAC design, structure-activity relationships, as well as in vitro and in vivo biological assessments.

Expert opinion: Despite considerable effort, no CDK2 inhibitor has yet received FDA approval for marketing due to poor selectivity and observed toxicity in clinical settings. Future research must prioritize the optimization of the selectivity, potency, and pharmacokinetics of CDK2 inhibitors and PROTACs. Moreover, exploring combination therapies incorporating CDK2 inhibitors with other targeted agents, or the design of multi-target inhibitors, presents significant promise for advancing cancer treatment strategies.

Introduction: Immobilized artificial membrane (IAM) chromatography is widely used in many aspects of drug discovery. It employs stationary phases, which contain phospholipids combining simulation of biological membranes with rapid measurements.

Areas covered: Advances in IAM stationary phases, chromatographic conditions and the underlying retention mechanism are discussed. The potential of IAM chromatography to model permeability and drug-membrane interactions as well as its use to estimate pharmacokinetic properties and toxicity endpoints including ecotoxicity, is outlined. Efforts to construct models for prediction IAM retention factors are presented.

Expert opinion: IAM chromatography, as a border case between partitioning and binding, has broadened its application from permeability studies to encompass processes involving tissue binding. Most IAM-based permeability models are hybrid models incorporating additional molecular descriptors, while for the estimation of pharmacokinetic properties and binding to off targets, IAM retention is combined with other biomimetic properties. However, for its integration into routine drug discovery protocols, reliable IAM prediction models implemented in relevant software should be developed, to enable its use in virtual screening and the design of new molecules. Conversely, preparation of new IAM columns with different phospholipids or mixed monomers offers enhanced flexibility and the potential to tailor the conditions according to the target property.

Introduction: Arrhythmias are disturbances in the normal rhythm of the heart and account for significant cardiovascular morbidity and mortality worldwide. Historically, preclinical research has been anchored in animal models, though physiological differences between these models and humans have limited their clinical translation. The discovery of human induced pluripotent stem cells (iPSC) and subsequent differentiation into cardiomyocyte has led to the development of new in vitro models of arrhythmias with the hope of a new pathway for both exploration of pathogenic variants and novel therapeutic discovery.

Areas covered: The authors describe the latest two-dimensional in vitro models of arrhythmias, several examples of the use of these models in drug development, and the role of gene editing when modeling diseases. They conclude by discussing the use of three-dimensional models in the study of arrythmias and the integration of computational technologies and machine learning with experimental technologies.

Expert opinion: Human iPSC-derived cardiomyocytes models have significant potential to augment disease modeling, drug discovery, and toxicity studies in preclinical development. While there is initial success with modeling arrhythmias, the field is still in its nascency and requires advances in maturation, cellular diversity, and readouts to emulate arrhythmias more accurately.

Introduction: Auranofin (AF) is a well-established, FDA-approved, antiarthritic gold drug that is currently being reevaluated for a variety of therapeutic indications through drug repurposing. AF has shown great promise as a potential anticancer agent and has been approved for a few clinical trials in cancer. The renewed interest in AF has led to extensive research into the design, preparation and biological evaluation of auranofin analogs, which may have an even better pharmacological profile than the parent drug.

Areas covered: This article reviews the strategies for chemical modification of the AF scaffold. Several auranofin analogs have been prepared and characterized for medical application in the field of cancer treatment over the last 20 years. Some emerging structure-function relationships are proposed and discussed.

Expert opinion: The chemical modification of the AF scaffold has been the subject of intense activity in recent years and this strategy has led to the preparation and evaluation of several AF analogs. The case of iodauranofin is a particularly promising example. The availability of homogeneous biological data for a group of AF derivatives allows some initial structure-function relationships to be proposed, which may inspire the design and synthesis of new and better AF analogs for cancer treatment.