Expert Opinion on Drug Discovery最新文献

Introduction: The use of laboratory animals is essential to understand the mechanisms underlying COPD and to discover and evaluate new drugs. However, the complex changes associated with the disease in humans are difficult to fully replicate in animal models.

Areas covered: This review examines the most recent literature on animal models of COPD and their implications for drug discovery and development.

Expert opinion: Recent advances in animal models include the introduction of transgenic mice with an increased propensity to develop COPD-associated features, such as emphysema, and animals exposed to relevant environmental agents other than cigarette smoke, in particular biomass smoke and other air pollutants. Other animal species, including zebrafish, pigs, ferrets and non-human primates, are also increasingly being used to gain insights into human COPD. Furthermore, three-dimensional organoids and humanized mouse models are emerging as technologies for evaluating novel therapeutics in more human-like models. However, despite these advances, no model has yet fully captured the heterogeneity and progression of COPD as observed in humans. Therefore, further research is needed to develop improved models incorporating humanized elements in experimental animals, that may better predict therapeutic responses in clinic settings and accelerate the development of new treatments for this debilitating disease.

Introduction: In 2023, the first exascale supercomputer was opened to the public in the US. With a demonstrated 1.1 exaflops of performance, Frontier represents an unprecedented breakthrough in high-performance computing (HPC). Currently, more (and more powerful) machines are being installed worldwide. Computer-aided drug design (CADD) is one of the fields of computational science that can greatly benefit from exascale computing for the benefit of the whole society. However, scaling CADD approaches to exploit exascale machines require new algorithmic and software solutions.

Areas covered: Here, the authors consider physics-based and machine learning (ML)-aided techniques for the design of small molecule binders capable of leveraging modern parallel computer architectures. Specifically, the authors focus on HPC-oriented large-scale applications from the past 3 years that were enabled by (pre)exascale supercomputers by running on up tothousands of accelerated nodes.

Expert opinion: In the area of ML, exascale computers can enable the training of generative models with unprecedented predictive power to design novel ligands, provided large amounts of high-quality data are available. Exascale computers could also unlock the potential of accurate ML-aided physics-based methods to boost the success rate of structure-based drug design campaigns. Currently, however, methodological developments are still required to allow routine large-scale applications of such rigorous approaches.

Introduction: China's pharmaceutical industry, which is historically centered around generic medicines, has largely transformed from imitation to innovation over the past three decades. Despite unprecedented progress, critical challenges remain such as insufficient indigenous research funding, underdeveloped academia-industry relationships, and significant barriers to market access.

Areas covered: This perspective examines the evolving pharmaceutical landscape of China, focusing on its participation in global clinical trials and the resultant new drug approvals. Data for this analysis was sourced from several databases (e.g. PharmCube, NextPharma, and PharmaGO), academic reports, and published literature, covering data up to 2024 (unless otherwise specified). This perspective highlights ongoing regulatory challenges, such as inconsistencies in product standards, and the approval processes relative to the U.S.A. and the European Union. There is also an urgent demand for sustained international investment and recognition, partially due to the recent changes in the geopolitical environment. This perspective also discusses China's efforts to implement accelerated approval pathways and foster multilateral development collaborations.

Expert opinion: China must align its regulatory policies more closely to the international norm to generate robust trial data that will be readily acceptable to the FDA and EMA. Continued investment in biologics as well as cell and gene therapy and artificial intelligence will drive innovation and enhance competitiveness. Additionally, strengthening the academia-industry collaboration is crucial to obtaining new leads through translational research. Ultimately, structural reforms are required to solidify the country's goal of becoming a major player in the global pharmaceutical market.

Introduction: Bladder cancer presents a significant health problem worldwide, with environmental and genetic factors contributing to its incidence. Histologically, it can be classified as carcinoma in situ, non-muscle invasive and muscle-invasive carcinoma, each one with distinct genetic alterations impacting prognosis and response to therapy. While traditional transurethral resection is commonly performed in carcinoma in situ and non-muscle invasive carcinoma, it often fails to prevent recurrence or progression to more aggressive phenotypes, leading to the frequent need for additional treatment such as intravesical chemotherapy or immunotherapy. Despite the advances made in recent years, treatment options for bladder cancer are still lacking due to the complex nature of this disease. So, animal models may hold potential for addressing these limitations, because they not only allow the study of disease progression but also the evaluation of therapies and the investigation of drug repositioning.

Areas covered: This review discusses the use of animal models over the past decade, highlighting key discoveries and discussing advantages and disadvantages for new drug discovery.

Expert opinion: Over the past decade animal models have been employed to evaluate new mechanisms underlying the responses to standard therapies, aiming to optimize bladder cancer treatment. The authors propose that molecular engineering techniques and AI may hold promise for the future development of more precise and effective targeted therapies in bladder cancer.

Introduction: Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate underlying principles and to derive rational optimization strategies.

Areas covered: In this review the authors summarize current best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism and excretion properties including the current knowledge of Drug-Drug interactions. Finally, the authors describe the Pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects.

Expert opinion: Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the Central Nervous System will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can enrich the field further and offer interesting alternative approaches.

Introduction: The history of treating chronic lymphocytic leukemia (CLL) inflected in 2014 with the Food and Drug Administration's (FDA) approval of ibrutinib, the first-in-class small molecule inhibitor of the Bruton's tyrosine kinase (BTK). Zanubrutinib is a 2^nd generation covalent BTK inhibitor developed and manufactured by BeiGene.

Areas covered: In this review, the authors trace the arc of zanubrutinib development from the preclinical phase through the two landmark phase 3 studies in the CLL space, ALPINE and SEQUOIA. The authors cover contemporary management strategies in CLL and highlight the areas of need that zanubrutinib was designed to mitigate.

Expert opinion: Zanubrutinib entered a fray of novel, exciting therapies for CLL. As the second of two 2^nd generation covalent BTK inhibitors its path to prominence in CLL management was narrow. Emphasis during development on kinase selectivity and enhanced bioavailability identified a molecule with superior efficacy and tolerability; hierarchical endpoints in trial design allowed for efficient acquisition of comparative data. Zanubrutinib is endorsed by the National Comprehensive Cancer Network as a preferred, category 1 recommended treatment choice for CLL. Future efforts in combination therapies and response-directed treatment breaks will hopefully lead to still further improvements in use.

Introduction: A substantial number of patients exhibit treatment-resistant depression (TRD), posing significant challenges to clinicians. The discovery of novel molecules or mechanisms that may underlie TRD pathogenesis and antidepressant actions is highly needed.

Areas covered: Using the PubMed database, the authors searched for emerging evidence of novel approaches for TRD based on experimental and human studies. Herein, the authors discuss the mechanisms underlying glutamatergic antagonists, modulators of the opioid system, and tryptamine-derivate psychedelics as well as the emerging platforms to investigate novel pharmacological targets for TRD. A search for clinical trials investigating novel agents and interventions for TRD was also conducted.

Expert opinion: The understanding of the multiple pathophysiological mechanisms involved in TRD may add further value to the effective treatment, contributing to a more personalized approach. Esketamine was approved for the treatment of TRD and novel drugs with rapid antidepressant actions such as psilocybin and buprenorphine have also been investigated as potential therapeutic strategies. Over the past decades, technological advances such as omics approaches have broadened our knowledge regarding molecular and genetic underpinnings of complex conditions like TRD. Omics approaches could open new avenues for investigating glial-mediated mechanisms, including their crosstalk with neurons, as therapeutic targets in TRD.

Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.

Areas covered: This perspective examines the current VS landscape, highlighting essential practices and identifying critical challenges, limitations, and common pitfalls. Using case studies and practices, this perspective aims to highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, the perspective explores common approaches for addressing pharmacodynamic and pharmacokinetic issues in optimizing VS hits.

Expert opinion: VS has become a tried-and-true technique of drug discovery due to the rapid advances in computational methods and machine learning (ML) over the past two decades. Although each VS workflow varies depending on the chosen approach and methodology, integrated strategies that combine biological and in silico data have consistently yielded higher success rates. Moreover, the widespread adoption of ML has enhanced the integration of VS into the drug discovery pipeline. However, the absence of standardized evaluation criteria hinders the objective assessment of VS studies' success and the identification of optimal adoption methods.

Introduction: Macromolecular X-ray crystallography (XRC), nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryoEM) are the primary techniques for determining atomic-level, three-dimensional structures of macromolecules essential for drug discovery. With advancements in artificial intelligence (AI) and cryoEM, the Protein Data Bank (PDB) is solidifying its role as a key resource for 3D macromolecular structures. These developments underscore the growing need for enhanced quality metrics and robust validation standards for experimental structures.

Areas covered: This review examines recent advancements in cryoEM for drug discovery, analyzing structure quality metrics, resolution improvements, metal-ligand and water molecule identification, and refinement software. It compares cryoEM with other techniques like XRC and NMR, emphasizing the global expansion of cryoEM facilities and its increasing significance in drug discovery.

Expert opinion: CryoEM is revolutionizing structural biology and drug discovery, particularly for large, complex structures in induced proximity and antibody-antigen interactions. It supports vaccine design, CAR T-cell optimization, gene editing, and gene therapy. Combined with AI, cryoEM enhances particle identification and 3D structure determination. With recent breakthroughs, cryoEM is emerging as a crucial tool in drug discovery, driving the development of new, effective therapies.