Application of Deep Learning for Studying NMDA Receptors.

Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood-brain barrier (BBB) permeability prediction, the quantitative structure-activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.

[The revolution of AI in drug development]. / L'intelligence artificielle, une révolution dans le développement des médicaments.

Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Title: L'intelligence artificielle, une révolution dans le développement des médicaments. Abstract: L'intelligence artificielle (IA) et l'apprentissage automatique produisent des modèles prédictifs qui aident à la prise de décisions dans le processus de découverte de nouveaux médicaments. Cette modélisation par ordinateur permet de représenter l'hétérogénéité d'une maladie, d'identifier des cibles thérapeutiques, de concevoir et optimiser des candidats-médicaments et d'évaluer ces médicaments sur des patients virtuels, ou des jumeaux numériques. En facilitant à la fois une connaissance détaillée des caractéristiques des patients et en prédisant les propriétés de multiples médicaments possibles, l'IA permet l'émergence d'une médecine de précision « computationnelle ¼ offrant des traitements parfaitement adaptés aux spécificités des patients.

Patient-Derived Xenografts: A Valuable Preclinical Model for Drug Development and Biomarker Discovery.

Patient-derived xenografts (PDXs), established by implanting patient tumor cells into immunodeficient mice, offer a platform for faithfully replicating human tumors. They closely mimic the histopathology, genomics, and drug sensitivity of patient tumors. This chapter highlights the versatile applications of PDXs, including studying tumor biology, metastasis, and chemoresistance, as well as their use in biomarker identification, drug screening, and personalized medicine. It also addresses challenges in using PDXs in cancer research, including variations in metastatic potential, lengthy establishment timelines, stromal changes, and limitations in immunocompromised models. Despite these challenges, PDXs remain invaluable tools guiding patient treatment and advancing preclinical drug development.

Patient-Derived Xenograft Models for Translational Prostate Cancer Research and Drug Development.

Patient-derived xenografts (PDXs) are a valuable preclinical research platform generated through transplantation of a patient's resected tumor into an immunodeficient or humanized mouse. PDXs serve as a high-fidelity avatar for both precision medicine and therapeutic testing against the cancer patient's disease state. While PDXs show mixed response to initial establishment, those that successfully engraft and can be sustained with serial passaging form a useful tool for basic and translational prostate cancer (PCa) research. While genetically engineered mouse (GEM) models and human cancer cell lines, and their xenografts, each play beneficial roles in discovery science and initial drug screening, PDX tumors are emerging as the gold standard approach for therapeutic proof-of-concept prior to entering clinical trial. PDXs are a powerful platform, with PCa PDXs shown to represent the original patient tumor cell population and architecture, histopathology, genomic and transcriptomic landscape, and heterogeneity. Furthermore, PDX response to anticancer drugs in mice has been closely correlated to the original patient's susceptibility to these treatments in the clinic. Several PDXs have been established and have undergone critical in-depth characterization at the cellular and molecular level across multiple PCa tumor subtypes representing both primary and metastatic patient tumors and their inherent levels of androgen responsiveness and/or treatment resistance, including androgen-sensitive, castration resistant, and neuroendocrine PCa. Multiple PDX networks and repositories have been generated for the collaborative and shared use of these vital translational cancer tools. Here we describe the creation of a PDX maintenance colony from an established well-characterized PDX, best practice for PDX maintenance in mice, and their subsequent application in preclinical drug testing. This chapter aims to serve as a go to resource for the preparation and adoption of PCa PDX models in the research laboratory and for their use as a valuable preclinical platform for translational research and therapeutic agent development.

Advances in sarcopenia: mechanisms, therapeutic targets, and intervention strategies.

Sarcopenia is a multifactorial condition characterized by loss of muscle mass. It poses significant health risks in older adults worldwide. Both pharmacological and non-pharmacological approaches are reported to address this disease. Certain dietary patterns, such as adequate energy intake and essential amino acids, have shown positive outcomes in preserving muscle function. Various medications, including myostatin inhibitors, growth hormones, and activin type II receptor inhibitors, have been evaluated for their effectiveness in managing sarcopenia. However, it is important to consider the variable efficacy and potential side effects associated with these treatments. There are currently no drugs approved by the Food and Drug Administration for sarcopenia. The ongoing research aims to develop more effective strategies in the future. Our review of research on disease mechanisms and drug development will be a valuable contribution to future research endeavors.

From molecules to visuals: Empowering drug discovery and development with mass spectrometry imaging.

Over the past three decades, mass spectrometry imaging (MSI) has emerged as a valuable tool for the spatial localization of drugs and metabolites directly from tissue surfaces without the need for labels. MSI offers molecular specificity, making it increasingly popular in the pharmaceutical industry compared to conventional imaging techniques like quantitative whole-body autoradiography (QWBA) and immunohistochemistry, which are unable to distinguish parent drugs from metabolites. Across the industry, there has been a consistent uptake in the utilization of MSI to investigate drug and metabolite distribution patterns, and the integration of MSI with omics technologies in preclinical investigations. To continue the further adoption of MSI in drug discovery and development, we believe there are two key areas that need to be addressed. First, there is a need for accurate quantification of analytes from MSI distribution studies. Second, there is a need for increased interactions with regulatory agencies for guidance on the utility and incorporation of MSI techniques in regulatory filings. Ongoing efforts are being made to address these areas, and it is hoped that MSI will gain broader utilization within the industry, thereby becoming a critical ingredient in driving drug discovery and development.

The landscape of small-molecule prodrugs.

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.

Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development.

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic.

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.

Targeted Gene Insertion: The Cutting Edge of CRISPR Drug Development with Hemophilia as a Highlight.

The remarkable advance in gene editing technology presents unparalleled opportunities for transforming medicine and finding cures for hereditary diseases. Human trials of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein-9 nuclease (Cas9)-based therapeutics have demonstrated promising results in disrupting or deleting target sequences to treat specific diseases. However, the potential of targeted gene insertion approaches, which offer distinct advantages over disruption/deletion methods, remains largely unexplored in human trials due to intricate technical obstacles and safety concerns. This paper reviews the recent advances in preclinical studies demonstrating in vivo targeted gene insertion for therapeutic benefits, targeting somatic solid tissues through systemic delivery. With a specific emphasis on hemophilia as a prominent disease model, we highlight advancements in insertion strategies, including considerations of DNA repair pathways, targeting site selection, and donor design. Furthermore, we discuss the complex challenges and recent breakthroughs that offer valuable insights for progressing towards clinical trials.

Accelerating drug development at Bristol Myers Squibb through innovation.

This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders.

INTRODUCTION: Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience. AREAS COVERED: The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states. EXPERT OPINION: Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties. AREAS COVERED: The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged. EXPERT OPINION: AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Using automated patch clamp electrophysiology platforms in ion channel drug discovery: an industry perspective.

INTRODUCTION: Automated patch clamp (APC) is now well established as a mature technology for ion channel drug discovery in academia, biotech and pharma companies, and in contract research organizations (CRO), for a variety of applications including channelopathy research, compound screening, target validation and cardiac safety testing. AREAS COVERED: Ion channels are an important class of drugged and approved drug targets. The authors present a review of the current state of ion channel drug discovery along with new and exciting developments in ion channel research involving APC. This includes topics such as native and iPSC-derived cells in ion channel drug discovery, channelopathy research, organellar and biologics in ion channel drug discovery. EXPERT OPINION: It is our belief that APC will continue to play a critical role in ion channel drug discovery, not only in 'classical' hit screening, target validation and cardiac safety testing, but extending these applications to include high throughput organellar recordings and optogenetics. In this way, with advancements in APC capabilities and applications, together with high resolution cryo-EM structures, ion channel drug discovery will be re-invigorated, leading to a growing list of ion channel ligands in clinical development.

Tuberculosis treatment-shortening.

Tuberculosis (TB) presents a significant global health concern, with ∼10 million people developing TB and 1.3 million people dying from the disease each year. The standard treatment regimen for drug-susceptible TB was between 6 and 9 months until recently, presenting a prolonged therapeutic duration compared with other infectious diseases. This is a long time for patients to adhere to the medication, consequently increasing the risk of developing drug-resistant Mycobacterium tuberculosis - a significant challenge in TB management globally. Therefore, the primary objective of contemporary TB drug development research is to shorten the treatment duration. This review comprehensively explores the strategies aimed at shortening TB treatment.