Expert Opinion on Drug Discovery最新文献

Introduction: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

Areas covered: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants ($k_{\mathrm{off}}$ and $k_{\mathrm{on}}$) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

Expert opinion: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Introduction: Peptide foldamers play a critical role in pharmaceutical research and biomedical applications. This review highlights recent (post-2020) advancements in novel foldamers, synthetic techniques, and their applications in pharmaceutical research.

Areas covered: The authors summarize the structures and applications of peptide foldamers such as α, β, γ-peptides, hydrocarbon-stapled peptides, urea-type foldamers, sulfonic-γ-amino acid foldamers, aromatic foldamers, and peptoids, which tackle the challenges of traditional peptide drugs. Regarding antimicrobial use, foldamers have shown progress in their potential against drug-resistant bacteria. In drug development, peptide foldamers have been used as drug delivery systems (DDS) and protein-protein interaction (PPI) inhibitors.

Expert opinion: These structures exhibit resistance to enzymatic degradation, are promising for therapeutic delivery, and disrupt crucial PPIs associated with diseases such as cancer with specificity, versatility, and stability, which are useful therapeutic properties. However, the complexity and cost of their synthesis, along with the necessity for thorough safety and efficacy assessments, necessitate extensive research and cross-sector collaboration. Advances in synthesis methods, computational modeling, and targeted delivery systems are essential for fully realizing the therapeutic potential of foldamers and integrating them into mainstream medical treatments.

Introduction: The effectiveness of Fragment-based drug design (FBDD) for targeting challenging therapeutic targets has been hindered by two factors: the small library size and the complexity of the fragment-to-hit optimization process. The DNA-encoded library (DEL) technology offers a compelling and robust high-throughput selection approach to potentially address these limitations.

Area covered: In this review, the authors propose the viewpoint that the DEL technology matches perfectly with the concept of FBDD to facilitate hit discovery. They begin by analyzing the technical limitations of FBDD from a medicinal chemistry perspective and explain why DEL may offer potential solutions to these limitations. Subsequently, they elaborate in detail on how the integration of DEL with FBDD works. In addition, they present case studies involving both de novo hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces.

Expert opinion: The future of DEL-based fragment discovery may be promoted by both technical advances and application scopes. From the technical aspect, expanding the chemical diversity of DEL will be essential to achieve success in fragment-based drug discovery. From the application scope side, DEL-based fragment discovery holds promise for tackling a series of challenging targets.

Introduction: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

Areas covered: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

Expert opinion: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Introduction: Benznidazole, the drug of choice for treating Chagas Disease (CD), has significant limitations, such as poor cure efficacy, mainly in the chronic phase of CD, association with side effects, and parasite resistance. Understanding parasite resistance to benznidazole is crucial for developing new drugs to treat CD.

Areas covered: Here, the authors review the current understanding of the molecular basis of benznidazole resistance. Furthermore, they discuss the state-of-the-art methods and critical outcomes employed to evaluate the efficacy of potential drugs against T. cruzi, aiming to select better compounds likely to succeed in the clinic. Finally, the authors describe the different strategies employed to overcome resistance to benznidazole and find effective new treatments for CD.

Expert opinion: Resistance to benznidazole is a complex phenomenon that occurs naturally among T. cruzi strains. The combination of compounds that inhibit different metabolic pathways of the parasite is an important strategy for developing a new chemotherapeutic protocol.

Introduction: Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved.

Areas covered: In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design.

Expert opinion: The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.