Expert Opinion on Drug Discovery最新文献

Introduction: Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide. This disease is complex and heterogeneous, influenced by a variety of genetic, epigenetic, and environmental factors that drive CRC initiation and progression. Despite advances in therapeutic strategies, the five-year survival rate for metastatic CRC is alarmingly low. Traditional two-dimensional (2D) cell culture systems have been the foundation of cancer research, but their inability to replicate the complex tumor microenvironment (TME) limits their effectiveness.

Areas covered: This paper explores the evolution of CRC models, starting with the limitations of traditional 2D cell culture systems and the significant advancements offered by 3D models. Additionally, it highlights 3D bioprinting and on-chip CRC models, which have enhanced the ability to mimic in vivo conditions.

Expert opinion: The transition to advanced 3D models represents a pivotal shift in CRC research, offering considerable improvements over the established 2D models. These models hold promise for the development of patient-specific models that better mimic in vivo conditions. However, the inherent complexity of CRC continues to pose challenges in developing models that can fully capture the disease's multifaceted nature. This complexity and high costs associated with these technologies, along with the need for standardized protocols, pose significant challenges to their widespread adoption.

Introduction: Highly pathogenic coronaviruses (CoVs), such as severe acute respiratory syndrome CoV (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and the most recent SARS-CoV-2 responsible for the COVID-19 pandemic, pose significant threats to human populations over the past two decades. These CoVs have caused a broad spectrum of clinical manifestations ranging from asymptomatic to severe distress syndromes (ARDS), resulting in high morbidity and mortality.

Areas covered: The accelerated advancements in antiviral drug discovery, spurred by the COVID-19 pandemic, have shed new light on the imperative to develop treatments effective against a broad spectrum of CoVs. This perspective discusses strategies and lessons learnt in targeting viral non-structural proteins, structural proteins, drug repurposing, and combinational approaches for the development of antivirals against CoVs.

Expert opinion: Drawing lessons from the pandemic, it becomes evident that the absence of efficient broad-spectrum antiviral drugs increases the vulnerability of public health systems to the potential onslaught by highly pathogenic CoVs. The rapid and sustained spread of novel CoVs can have devastating consequences without effective and specifically targeted treatments. Prioritizing the effective development of broad-spectrum antivirals is imperative for bolstering the resilience of public health systems and mitigating the potential impact of future highly pathogenic CoVs.

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: 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: Although there are numerous treatment options already available for epilepsy, over 30% of patients remain resistant to these antiseizure medications (ASMs). Historically, ASM discovery has relied on the demonstration of efficacy through the use of 'traditional' acute in vivo seizure models (e.g. maximal electroshock, subcutaneous pentylenetetrazol, and kindling). However, advances in genetic sequencing technologies and remaining medical needs for people with treatment-resistant epilepsy or special patient populations have encouraged recent efforts to identify novel compounds in syndrome-specific models of epilepsy. Syndrome-specific models, including Scn1a variant models of Dravet syndrome and APP/PS1 mice associated with familial early-onset Alzheimer's disease, have already led to the discovery of two mechanistically novel treatments for developmental and epileptic encephalopathies (DEEs), namely cannabidiol and soticlestat, respectively.

Areas covered: In this review, the authors discuss how it is likely that next-generation drug discovery efforts for epilepsy will more comprehensively integrate syndrome-specific epilepsy models into early drug discovery providing the reader with their expert perspectives.

Expert opinion: The percentage of patients with pharmacoresistant epilepsy has remained unchanged despite over 30 marketed ASMs. Consequently, there is a high unmet need to reinvent and revise discovery strategies to more effectively address the remaining needs of patients with specific epilepsy syndromes, including drug-resistant epilepsy and DEEs.

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: 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.