Expert Opinion on Drug Discovery最新文献

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: 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: Determining whether a new drug is a substrate, inhibitor or inducer of efflux or uptake membrane transporters has become a routine process during drug discovery and development. In vitro assays are utilized to establish whether a new drug has the potential to be an object (substrate) or precipitant (inhibitor, inducer) in transporter-mediated clinical drug-drug interactions. The findings from these in vitro experiments are then used to determine whether further in vivo drug interaction studies are necessary for a new drug.

Areas covered: This article provides an update on in vitro transporter assays, focusing on new uses of transfected cells, time-dependent inhibition, transporter induction, and complex model systems.

Expert opinion: The newer in vitro assays add to the toolbox in defining new drugs as transporter substrates, inhibitors, or inducers. Complex models such as spheroids, organoids, and microphysiological systems require standardization and further research with model transporter substrates and inhibitors. In drug discovery, the more traditional transporter assays may be employed as substrate and inhibitor screening assays. In drug development, more complex cell models can be employed in later drug development to better understand how transporter(s) are involved in the absorption, distribution, and excretion of new drugs.

Introduction: Due to their faithful recapitulation of human disease, nonhuman primates (NHPs) are considered the gold standard for evaluating drugs against Ebolavirus and other filoviruses. The long-term goal is to reduce the reliance on NHPs with more ethical alternatives. In silico simulations and organoid models have the potential to revolutionize drug testing by providing accurate, human-based systems that mimic disease processes and drug responses without the ethical concerns associated with animal testing. However, as these emerging technologies are still in their developmental infancy, NHP models are presently needed for late-stage evaluation of filovirus vaccines and drugs, as they provide critical insights into the efficacy and safety of new medical countermeasures.

Areas covered: In this review, the authors introduce available NHP models and examine the existing literature on drug discovery for all medically significant filoviruses in corresponding models.

Expert opinion: A deliberate shift toward animal-free models is desired to align with the 3Rs of animal research. In the short term, the use of NHP models can be refined and reduced by enhancing replicability and publishing negative data. Replacement involves a gradual transition, beginning with the selection and optimization of better small animal models; advancing organoid systems, and using in silico models to accurately predict immunological outcomes.

Introduction: For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

Areas covered: The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

Expert opinion: The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.