Expert Opinion on Drug Discovery最新文献

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: 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: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored.

Areas covered: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective.

Expert opinion: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Introduction: Lipophilic efficiency (LipE) and lipophilic metabolic efficiency (LipMetE) are valuable tools that can be utilized as part of a multiparameter optimization process to advance a hit to a clinical quality compound.

Areas covered: This review covers recent, effective use cases of LipE and LipMetE that have been published in the literature over the past 5 years. These use cases resulted in the delivery of high-quality molecules that were brought forward to in vivo work and/or to clinical studies. The authors discuss best-practices for using LipE and LipMetE analysis, combined with lipophilicity-focused compound design strategies, to increase the speed and effectiveness of the hit to clinical quality compound optimization process.

Expert opinion: It has become well established that increasing LipE and LipMetE within a series of analogs facilitates the improvement of broad selectivity, clearance, solubility, and permeability and, through this optimization, also facilitates the achievement of desired pharmacokinetic properties, efficacy, and tolerability. Within this article, we discuss lipophilic efficiency-focused optimization as a tool to yield high-quality potential clinical candidates. It is suggested that LipE/LipMetE-focused optimization can facilitate and accelerate the drug-discovery process.

Introduction: Gastric cancer remains a formidable challenge in oncology with high mortality rates and few advancements in treatment. Claudin-18.2 (CLDN18.2) is a tight junction protein primarily expressed in the stomach and is frequently overexpressed in certain subsets of gastric cancers. Targeting CLDN18.2 with monoclonal antibodies, such as zolbetuximab (IMAB362), has shown promising efficacy results in combination with chemotherapy.

Areas covered: The molecular cell biology of CLDN18.2 is discussed along with studies demonstrating the utility of CLDN18.2 expression as a biomarker and therapeutic target. Important clinical studies are reviewed, including Phase III trials, SPOTLIGHT and GLOW, which demonstrate the efficacy of zolbetuximab in combination with chemotherapy in patients with CLDN18.2-positive advanced gastric cancer.

Expert opinion: CLDN18.2 is involved in gastric differentiation through maintenance of epithelial barrier function and coordination of signaling pathways, and its expression in gastric cancers reflects a 'gastric differentiation' program. Targeting Claudin-18.2 represents the first gastric cancer specific 'targeted' treatment. Further studies are needed to determine its role within current gastric cancer treatment sequencing, including HER2-targeted therapies and immunotherapies. Management strategies will also be needed to better mitigate zolbetuximab-related treatment side effects, including gastrointestinal (GI) toxicities.

Introduction: Guanine nucleotide exchange factors (GEFs) regulate the activation of small GTPases (G proteins) of the Ras superfamily proteins controlling cellular functions. Ras superfamily proteins act as 'molecular switches' that are turned 'ON' by guanine exchange. There are five major groups of Ras family GTPases: Ras, Ran, Rho, Rab and Arf, with a variety of different GEFs regulating their GTP loading. GEFs have been implicated in various diseases including cancer. This makes GEFs attractive targets to modulate signaling networks controlled by small GTPases.

Areas covered: In this review, the roles and mechanisms of GEFs in malignancy are outlined. The mechanism of guanine exchange activity by GEFs on a small GTPase is illustrated. Then, some examples of GEFs that are significant in cancer are presented with a discussion on recent progress in therapeutic targeting efforts using a variety of approaches.

Expert opinion: Recently, GEFs have emerged as potential therapeutic targets for novel cancer drug development. Targeting small GTPases is challenging; thus, targeting their activation by GEFs is a promising strategy. Most GEF-targeted drugs are still in preclinical development. A deeper biological understanding of the underlying mechanisms of GEF activity and utilizing advanced technology are necessary to enhance drug discovery for GEFs in cancer.

Introduction: Cyclic peptides are an established class of pharmaceuticals, with the ability to bind to a broader range of protein targets than traditional small molecules while also being capable of oral availability and cell penetration. Historically, cyclic peptide drugs have been discovered almost exclusively through natural product mining approaches; however, the last two decades have seen the development of display screening approaches capable of rapidly identifying de novo (i.e. not natural product derived) cyclic peptide ligands to targets of interest.

Areas covered: In this review, the authors describe the current clinical landscape for cyclic peptide pharmaceuticals. This article focuses on the discovery approaches that have led to the development of different classes of molecules and how the development of newer technologies, particularly phage and mRNA display, has broadened the clinical applicability of such molecules.

Expert opinion: The field of de novo cyclic peptide drug discovery is reaching maturity, with the first drugs identified through display screening approaches reaching the market in recent years. Many more are in clinical trials; however, significant technical challenges remain. Technological improvements will be required over the coming years to facilitate the identification of membrane permeable cyclic peptides capable of oral availability and targeting intracellular proteins.