Machine Learning in Early Prediction of Metabolism of Drugs.

Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bottleneck in the early identification of toxic metabolites or biotransformation pathways that can affect elimination of the drug and potentially hinder the development of future new drugs. Metabolism prediction can be addressed with the application of ML models trained on large and validated dataset, from early stages of lead optimization to latest stage of drug development. ML methods rely on molecular descriptors that allow to identify and learn chemical and molecular features to predict sites of metabolism (SoMs) or activity associated with mechanism of inhibition (e.g., CYP inhibition). The application of ML methods in the prediction of drug metabolism represents a powerful resource to be exploited during drug discovery and development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost, and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3R principles within the Big Data era.

Development and Prospects of Furin Inhibitors for Therapeutic Applications.

Furin, a serine protease enzyme located in the Golgi apparatus of animal cells, plays a crucial role in cleaving precursor proteins into their mature, active forms. It is ubiquitously expressed across various tissues, including the brain, lungs, gastrointestinal tract, liver, pancreas, and reproductive organs. Since its discovery in 1990, furin has been recognized as a significant therapeutic target, leading to the active development of furin inhibitors for potential use in antiviral, antibacterial, anticancer, and other therapeutic applications. This review provides a comprehensive overview of the progress in the development and characterization of furin inhibitors, encompassing peptides, linear and macrocyclic peptidomimetics, and non-peptide compounds, highlighting their potential in the treatment of both infectious and non-infectious diseases.

In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review.

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM.

Enhancing Drug Discovery and Development through the Integration of Medicinal Chemistry, Chemical Biology, and Academia-Industry Partnerships: Insights from Roche's Endocannabinoid System Projects.

The endocannabinoid system (ECS) is a critical regulatory network composed of endogenous cannabinoids (eCBs), their synthesizing and degrading enzymes, and associated receptors. It is integral to maintaining homeostasis and orchestrating key functions within the central nervous and immune systems. Given its therapeutic significance, we have launched a series of drug discovery endeavors aimed at ECS targets, including peroxisome proliferator-activated receptors (PPARs), cannabinoid receptors types 1 (CB1R) and 2 (CB2R), and monoacylglycerol lipase (MAGL), addressing a wide array of medical needs. The pursuit of new therapeutic agents has been enhanced by the creation of specialized labeled chemical probes, which aid in target localization, mechanistic studies, assay development, and the establishment of biomarkers for target engagement. By fusing medicinal chemistry with chemical biology in a comprehensive, translational end-to-end drug discovery strategy, we have expedited the development of novel therapeutics. Additionally, this strategy promises to foster highly productive partnerships between industry and academia, as will be illustrated through various examples.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects.

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

AB067. New drug development for the use of PARP1-E2F1 transcriptional inhibitors in the treatment of glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most malignant brain tumor and ranks among the most lethal of all human cancers, without improvements in survival over the last 30 years. Data obtained in our group suggest that PARP1, a well-known DNA-repairing protein, could also play a key role in the regulation of cell cycle through its interaction with the transcription factor E2F1. Therefore, considering that most oncogenic processes are associated with cell cycle deregulation, we hypothesized that disruption of PARP1-E2F1 interaction would provide a novel therapeutic approach to different types of cancer. METHODS: The identification of novel compounds disrupting PARP1-E2F1 interaction was carried out by combining in silico and in vitro screening, using a rational drug design. The virtual screen was performed using a molecular library of several million compounds at the selected target site, using AtomNet® (Atomwise, San Francisco, CA, USA), the first deep learning neural network for structure-based drug design and discovery. Since there is no complete structural information of the PARP1-E2F1 protein-protein interaction, a homologous structure of the BRCT domain of BRCA1 complex with the phospho-peptide (PDBID: 1T2V) was used to identify the potential binding interface of BRCT domain of PARP-1 (PDBID: 2COK) and the E2F1 protein. Top scoring compounds were clustered and filtered to obtain a final subset of 83 compounds that were incorporated to our in vitro screening, which included both transcriptional E2F1 activity and survival studies. Complete culture medium supplemented with the compounds selected in the in silico screening (10 µM) were added and incubated for 24 hours. E2F1 activity was observed by measuring luminescence. For the viability assay, the fluorescence reading was performed (excitation 544 nm and emission 590 nm). RESULTS: The in silico and in vitro screening resulted in 12 compounds that inhibited E2F1 transcriptional activity and significantly reduced cell number. The highest inhibition of both E2F1 transcriptional activity and cell growth was observed with compound 3797, which was selected for further studies. CONCLUSIONS: Both in silico and in vitro results indicate that inhibition of PARP1-E2F1 transcriptional activity may provide a new rationale for designing novel therapeutic approaches for the treatment of GBM.

Improving glioma drug delivery: A multifaceted approach for glioma drug development.

Glioma is one of the most common central nervous system (CNS) cancers that can be found within the brain and the spinal cord. One of the pressing issues plaguing the development of therapeutics for glioma originates from the selective and semipermeable CNS membranes: the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB). It is difficult to bypass these membranes and target the desired cancerous tissue because the purpose of the BBB and BSCB is to filter toxins and foreign material from invading CNS spaces. There are currently four varieties of Food and Drug Administration (FDA)-approved drug treatment for glioma; yet these therapies have limitations including, but not limited to, relatively low transmission through the BBB/BSCB, despite pharmacokinetic characteristics that allow them to cross the barriers. Steps must be taken to improve the development of novel and repurposed glioma treatments through the consideration of pharmacological profiles and innovative drug delivery techniques. This review addresses current FDA-approved glioma treatments' gaps, shortcomings, and challenges. We then outline how incorporating computational BBB/BSCB models and innovative drug delivery mechanisms will help motivate clinical advancements in glioma drug delivery. Ultimately, considering these attributes will improve the process of novel and repurposed drug development in glioma and the efficacy of glioma treatment.

Drug Development Failure: How GLP-1 Development Was Abandoned in 1990.

Many factors determine whether and when a class of therapeutic agents will be successfully developed and brought to market, and historians of science, entrepreneurs, drug developers, and clinicians should be interested in accounts of both successes and failures. Successes induce many participants and observers to document them, whereas failed efforts are often lost to history, in part because involved parties are typically unmotivated to document their failures. The GLP-1 class of drugs for diabetes and obesity have emerged over the past decade as clinical and financial blockbusters, perhaps soon becoming the highest single source of revenue for the pharmaceutical industry (Berk 2023). In that context, it is instructive to tell the story of the first commercial effort to develop this class of drugs for metabolic disease, and how, despite remarkable early success, the work was abandoned in 1990. Told by a key participant in the effort, this story documents history that would otherwise be lost and suggests a number of lessons about drug development that remain relevant today.

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

[Chasing New Cancer Treatments: Current Status and Future Development of Boron Neutron Capture Therapy].

Boron neutron capture therapy (BNCT) is expected to be a promising next-generation cancer treatment. In 2020, Japan, which has led the research on this treatment modality, was the first country in the world to approve BNCT. The boron agents that have been clinically applied in BNCT include a caged boron compound (mercaptoundecahydrododecaborate: BSH) and a boron-containing amino acid (p-boronophenylalanine: BPA). In particular, the BPA preparation Steboronine® is the only approved drug for BNCT. However, the problem with BPA is that it is poorly retained in the tumor and has very low solubility in water. This cannot be overlooked for BNCT, which requires large amounts of boron in the tumor. The high dosage volume, together with low tumor retention, leads to reduced therapeutic efficacy and increased physical burden on the patient. In the case of BSH, its insufficient penetration into the tumor is problematic. Based on drug delivery system (DDS) technology, we have developed a next-generation boron pharmaceutical superior to Steboronine®. Our approach involves the redevelopment of BPA using innovative ionic liquid formulation technology. Here, we describe previous boron agents and introduce our recent efforts in the development of boron compounds.

Chemistry, Pharmacology and Therapeutic Potential of Decursin: A Promising Natural Lead for New Drug Discovery and Development.

Decursin is a pyranocoumarin compounds which are rare secondary metabolic plant products, isolated from the roots of Angelica gigas (A. gigas). The native Korean species Angelica gigas Nakai (AGN) is widely used as a remedy for a variety of medical conditions including hematopoiesis, improving women's circulation, as sedatives, analgesics and tonic. It is unique because of the presence of substantial amounts of pyranocoumarins including decursin, decursinol, and decursinol angelate. In this review, we provide a comprehensive insight into the distribution, morphology, and chemical composition of A. gigas. A detailed discussion regarding the biological applications of decursin based on the literature retrieved from PubMed, ScienceDirect, Scopus, and Google Scholar from 2000 to the present has been discussed. Both in vitro and in vivo studies have demonstrated that decursin has potential neuroprotective, anti-inflammatory, anti-melanogenic, anti-angiogenic, antioxidant, and anti-visceral properties. Mechanistic findings establish its significance in regulating important signalling pathways, triggering apoptosis, and preventing metastasis in different cancer types. The review additionally addressed the isolation methods, biosynthesis, physiochemical characteristics, toxicity and pharmacokinetic profile of decursin. The present state of clinical studies including A. gigas is investigated, emphasizing its advancements and possibilities in the field of translational medicine.

Clinical Utility of Rilonacept for the Treatment of Recurrent Pericarditis: Design, Development, and Place in Therapy.

Recurrent pericarditis (RP) has been traditionally regarded as a "nightmare" for both clinicians and patients. Until approximately a decade ago, available treatments were thin on the ground with non-steroidal anti-inflammatory medications, glucocorticoids, colchicine, and classical immunosuppressants being the only options. The first important step in the tale of RP was the advent of colchicine in clinical practice, which has been shown to halve the rate of first and subsequent pericarditis recurrences. The second major breakthrough advance in this setting was the introduction of interleukin-1 inhibitors based on the recently unveiled autoinflammatory nature of pericarditis. At present, anti-interleukin-1 inhibitors available for clinical use in patients with refractory RP include anakinra and rilonacept, with the latter having obtained FDA approval for this indication. Apart from the remarkable efficacy and good safety profile which is a common feature of all anti-interleukin-1 compounds, rilonacept has the advantage of weekly administration (instead of daily compared to anakinra) which is important in terms of adherence to treatment and improved quality of life albeit at the expense of a higher cost. This review aims to summarize the available evidence on the role of rilonacept in the treatment of RP and the reduction of the recurrences risk.

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists.

ABSTRACT: Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Advancing pediatric drug development in South Asia: Current landscape and vision for the future.

ABSTRACT: The manuscript summarizes the outcomes of a one-day conference by the South Asian College of American College of Clinical Pharmacology (SAC-ACCP) in July 2023, at Bhopal. The theme of the conference was "Advancing pediatric drug development in South Asia." SAC-ACCP organized this event in Bhopal to foster the discipline of clinical pharmacology and to motivate researchers and physicians in the in the central part of India. The conference featured presentations on regional approaches to pediatric drug development in Asia by pediatric scientific experts from the pharmaceutical industry, regulatory agencies, as well as independent consultancies. The speakers highlighted several important aspects of the evolving regulatory landscape in India and proposed numerous actionable steps in acceleration of pediatric drug development. This commentary provides insights from presentations and the panel discussion at this conference and also makes an attempt to connect to similar discussions that occurred at the SAC-ACCP drug development conference in 2017.

Advancing global antibiotic research, development and access.

The pipeline of new antibiotics is insufficient to keep pace with the growing global burden of drug-resistant infections. Substantial economic challenges discourage private investment in antibiotic research and development (R&D), with a decline in the number of companies and researchers working in the field. Compounding these issues, many countries (from low income to high income) face a growing crisis of antibiotic shortages and inequitable access to existing and emerging treatments. This has led to an increasing role for public and philanthropic funding in supporting antibiotic R&D via the creation of nonprofit public-private partnerships, including Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator (CARB-X) and the Global Antibiotic Research and Development Partnership (GARDP), industry support for the AMR Action Fund, and pilot schemes to evaluate and reimburse antibiotics in innovative ways. Now is the time to raise the urgency, ambition and commitments of the world's leaders to fully support the antibiotic R&D ecosystem, incentivizing all sectors to conduct public health-driven antibiotic R&D and make effective antibiotics accessible to all who need them.

Serotonergic Drugs for the Treatment of Attention-Deficit/Hyperactivity Disorder: A Review of Past Development, Pitfalls and Failures, and a Look to the Future.

Serotonin has been implicated in the neurobiology of attention-deficit/hyperactivity disorder (ADHD) due to its association with impulsivity, attention, and emotional regulation. Many compounds with serotonergic properties have been evaluated in ADHD, but few have been approved by regulatory authorities. Utilizing a search of public databases, we identified interventions studied in ADHD. Prescribing information and peer-reviewed and gray literature helped us to determine which compounds had an underlying mechanism of action associated with changing serotonin levels. Of the 24 compounds that met the search criteria, 16 had either failed clinical studies in an ADHD population or had been discontinued from future development. The available evidence was assessed to identify the developmental history of drugs with serotonergic activity and the outlook for new ADHD drug candidates targeting serotonin. Several treatment candidates floundered due to an inability to balance effectiveness with safety, underscoring the potential importance of potency, and selectivity. Ongoing drug development includes compounds with multimodal mechanisms of action targeting neurotransmission across serotonin, norepinephrine, and dopamine pathways; it appears likely that treatment which balances competing and complementary monoamine effects may provide improved outcomes for patients. It is hoped that continuing research into ADHD treatment will produce new therapeutic options targeting the serotonergic system, which can positively impact a wide range of symptoms, including mood, anxiety, and sleep as well as attention and hyperactivity.

A Bayesian latent-subgroup platform design for dose optimization.

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.