Expert Opinion on Drug Discovery最新文献

Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.

Areas covered: In this perspective, the authors discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models, and which issue-specific mitigations have been effective. Next, they point out the challenges faced by uncertainty quantification techniques aimed at enhancing and trusting the predictions from these AI models. They also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, the authors explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated by gaining more prospective experience.

Expert opinion: AI models are often developed to excel on retrospective benchmarks unlikely to anticipate their prospective performance. As a result, only a few of these models are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). The authors have discussed what can go wrong in practice with AI for drug discovery. The authors hope that this will help inform the decisions of editors, funders investors, and researchers working in this area.

Introduction: The autoimmune hair loss condition alopecia areata (AA) exacts a substantial psychological and socioeconomic toll on patients. Biotechnology companies, dermatology clinics, and research institutions are dedicated to understanding AA pathogenesis and developing new therapeutic approaches. Despite recent efforts, many knowledge gaps persist, and multiple treatment development avenues remain unexplored.

Areas covered: This review summarizes key AA disease mechanisms, current therapeutic methods, and emerging treatments, including Janus Kinase (JAK) inhibitors. The authors determine that innovative drug discovery strategies for AA are still needed due to continued unmet medical needs and the limited efficacy of current and emerging therapeutics. For prospective AA treatment developers, the authors identify the pre-clinical disease models available, their advantages, and limitations. Further, they outline treatment development opportunities that remain largely unmapped.

Expert opinion: While recent advancements in AA therapeutics are promising, challenges remain, including the lack of consistent treatment efficacy, long-term use and safety issues, drug costs, and patient compliance. Future drug development research should focus on patient stratification utilizing robust biomarkers of AA disease activity and improved quantification of treatment response. Investigating superior modes of drug application and developing combination therapies may further improve outcomes. Spirited innovation will be needed to advance more effective treatments for AA.

Introduction: Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead compound's pharmacodynamic, pharmacokinetic, and physiochemical properties. Scaffold hopping opens up fresh molecular territory beyond established patented chemical domains.

Area covered: The authors present the scaffold hopping-based drug design strategies for dual inhibitory antitumor structural templates in this review. Minor modifications, structure rigidification and simplification (ring-closing and opening), and complete structural overhauls were the strategies employed by the medicinal chemist to generate a library of bifunctional inhibitors. In addition, the review presents an overview of the computational methods of scaffold hopping (software and programs) and organopalladium catalysis leveraged for the synthesis of templates designed via scaffold hopping.

Expert opinion: The medicinal chemist has demonstrated remarkable prowess in furnishing dual inhibitory antitumor chemical architectures. Scaffold hopping-based drug design strategies have yielded a plethora of pharmacodynamically superior dual modulatory antitumor agents. An integrated approach involving computational advancements, synthetic methodology advancements, and conventional drug design strategies is required to increase the number of scaffold-hopping-assisted drug discovery campaigns.

Background: Targeting the enzyme L-Arginine:glycine amidinotransferase (AGAT) to reduce the formation of guanidinoacetate (GAA) in patients with guanidinoacetate methyltransferase (GAMT) deficiency, we attempted to identify drugs for repurposing that reduce the expression of AGAT via transcriptional inhibition.

Research design and methods: The authors applied a HeLa cell line stably expressing AGAT promoter and firefly luciferase reporter for high-content screening and secondary screening. For further assessment, the authors integrated Nanoluc luciferase as a reporter into the endogenous AGAT gene in HAP1 cell lines and used the human immortalized cell line RH30 as model of GAMT deficiency.

Results: Screening 6,000 drugs and drug-like compounds, the authors identified 43 and 34 high-score candidates as inhibitors and inducers of AGAT promoter-reporter expression, respectively. After further deselection considering dose response, drug toxicity, topical formulations, price, and accessibility, the authors assessed seven candidates and found none of them demonstrating efficacy in HAP1 and RH30 cells and warranting further assessment.

Conclusion: The selection of the test models is crucial for screening of gene repressor drugs. Almost all drugs with an impact on gene expression had off-target effects. It is unlikely to find drugs that are selective inhibitors of AGAT expression, rendering pharmacological AGAT gene repression a risky approach for the treatment of GAMT deficiency.

Introduction: Targeted protein degradation (TPD) has emerged as an innovative therapeutic strategy through selective degradation of specific proteins by harnessing the cellular ubiquitin-proteasome system (UPS), which involves over 600 E3 ubiquitin ligases. Recent proteome profiling reported tumor-specific E3 ligases in human. Development of those tumor-specific E3 ligase ligands would provide a solution for tumor-specific TPD for effective cancer treatment.

Areas covered: This review provides a comprehensive list of E3 ligases found only in specific types of tumor from public databases and highlights examples of their ligands discovered through fragment-based approaches. It details their discovery process and potential applications for precise TPD and effective cancer treatments.

Expert opinion: Current TPD strategies using proteolysis-targeting chimeras (PROTACs) primarily utilize general E3 ligases, such as CRBN and VHL. Since these E3 ligases demonstrate effective protein degradation activity in most human cell types, CRBN and VHL-based PROTACs can exhibit undesired TPD in off-target tissues, which often leads to the side effects. Therefore, developing tumor-specific E3 ligase ligands can be crucial for effective cancer treatments. Fragment-based ligand discovery (FBLD) approaches would accelerate the identification of these tumor-specific E3 ligase ligands and associated PROTACs, thereby advancing the field of targeted cancer therapies.

Introduction: Myeloproliferative neoplasms (MPNs) are rare hematopoietic disorders driven by mutations in the JAK-STAT signaling pathway genes. While JAK2 inhibitors have transformed MPN treatment, they do not eliminate the malignant clone or prevent disease progression in most patients. This limitation underscores the need for more effective therapies.

Area covered: This review examines the evolution of JAK2 inhibitors for treating MPNs. Current JAK2 inhibitors primarily function as type I inhibitors, targeting the active kinase conformation, but their effectiveness is limited by ongoing JAK-STAT signaling. To overcome these limitations, next-generation therapies, such as type II JAK2 inhibitors and pseudokinase domain inhibitors, are being developed to target inactive kinase conformations and alternative signaling pathways. Furthermore, combination therapies with PI3K, mTOR, CDK4/6 inhibitors, and epigenetic modulators are being investigated for their potential synergistic effects, aiming for deeper and more durable responses in MPN patients.

Expert opinion: Next-generation JAK2 inhibitors are needed to enhance current MPNs treatments by overcoming resistance, improving selectivity, targeting specific patient groups, and exploring combination therapies. Addressing challenges in drug design, preclinical testing, and clinical trials is crucial. Developing dual or multiple inhibitors targeting JAK2 and other MPN-related pathways is urgent to address complex signaling networks and improve efficacy.

Introduction: This review encapsulates the recent strides in the development of non-nucleoside reverse transcriptase inhibitors (NNRTIs) for HIV treatment, focusing on the novel structural designs that promise to overcome limitations of existing therapies, such as drug resistance and toxicity.

Areas covered: We underscore the application of computational chemistry and structure-based drug design in refining NNRTIs with enhanced potency and safety.

Expert opinion: Highlighting the emergence of diverse chemical scaffolds like diarylpyrimidines, indoles, DABOs and HEPTs, the review reveals compounds with nanomolar efficacy and improved pharmacokinetics. The integration of artificial intelligence in drug discovery is poised to accelerate the evolution of NNRTIs, laying the foundation for addressing drug resistance in the era of anti-HIV therapy through innovative designs and multi-target strategies.

Introduction: Aptamers refer to short ssDNA/RNA sequences that target small molecules, proteins, or cells. Aptamers have significantly advanced diagnostic applications, including biosensors for detecting specific biomarkers, state-of-the-art imaging, and point-of-care technology. Molecular computation helps identify aptamers with high-binding affinity, enabling high-throughput screening, predicting 3D structures, optimizing aptamers for improved stability, specificity, and complex target interactions.

Area covered: Aptamers are versatile in the development of specific and sensitive diagnostics. However, there needs to be more understanding of the precise workflow that integrates sequence, structure, and interaction with the target. In this review, the author discusses how significant progress has been made in aptamer discovery using bioinformatics for sequence analysis, docking to model interactions, and MD simulations to account for dynamicity and predict free-energy. Furthermore, the author discusses how quantum chemical calculations are critical for modelling electronic structures and assignin spectroscopic signals.

Expert opinion: Incorporating machine learning into the aptamer discovery brings a transformative advancement. With NGS datasets, SELEX, and experimental structures, the implementation of newer workflows yields aptamers with improved binding affinity. Leveraging transfer learning to models using experimental structures and aptamer sequences expands the aptamer design space significantly. As ML continues to evolve, it is poised to become central in accelerating aptamer discovery for biomedical applications in the next 5 years.