Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.

The study of drug-target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug-target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

Journey from lab to clinic: Design, preclinical, and clinical development of systemic, targeted dendrimer-N-acetylcysteine (D-NAC) nanomedicines.

Drug discovery is challenging task with numerous obstacles in translating drug candidates into clinical products. Dendrimers are highly adaptable nanostructured polymers with significant potential to improve the chances of clinical success for drugs. Yet, dendrimer-based drug products are still in their infancy. However, Hydroxyl polyamidoamine (PAMAM) dendrimers showed significant promise in drug discovery efforts, owning their remarkable potential to selectively target and deliver drugs specifically to activated microglia and astrocytes at the site of brain injury in several preclinical models. After a decade's worth of academic research and pre-clinical efforts, the hydroxyl PAMAM dendrimer-N-acetyl cysteine conjugate (OP-101) nanomedicine has made a significant advancement in the field of nanomedicine and targeted delivery. The OP-101 conjugate, primarily developed and validated in academic labs, has now entered clinical trials as a potential treatment for hyperinflammation in hospitalized adults with severe COVID-19 through Ashvattha Therapeutics. This chapter, we delve into the journey of the hydroxyl PAMAM dendrimer-N-acetylcysteine (NAC) OP-101 formulation from the laboratory to the clinic. It will specifically focus on the design, synthesis, preclinical, and clinical development of OP-101, highlighting the potential it holds for the future of medicine and the positive Phase 2a results for treating severe COVID-19.

Silvestrol, a potent anticancer agent with unfavourable pharmacokinetics: Current knowledge on its pharmacological properties and future directions for the development of novel drugs.

Cancer remains a leading cause of death, with increasing incidence. Conventional treatments offer limited efficacy and cause significant side effects, hence novel drugs with improved pharmacological properties and safety are required. Silvestrol (SLV) is a flavagline derived from some plants of the Aglaia genus that has shown potent anticancer effects, warranting further study. Despite its efficacy in inhibiting the growth of several types of cancer cells, SLV is characterized by an unfavorable pharmacokinetics that hamper its use as a drug. A consistent research over the recent years has led to develop novel SLV derivatives with comparable pharmacodynamics and an ameliorated pharmacokinetic profile, demonstrating potential applications in the clinical management of cancer. This comprehensive review aims to highlight the most recent data available on SLV and its synthetic derivatives, addressing their pharmacological profile and therapeutic potential in cancer treatment. A systematic literature review of both in vitro and in vivo studies focusing on anticancer effects, pharmacodynamics, and pharmacokinetics of these compounds is presented. Overall, literature data highlight that rationale chemical modifications of SLV are critical for the development of novel drugs with high efficacy on a broad variety of cancers and improved bioavailability in vivo. Nevertheless, SLV analogues need to be further studied to better understand their mechanisms of action, which can be partially different to SLV. Furthermore, clinical research is still required to assess their efficacy in humans and their safety.

The history and potential future of monoclonal antibody therapeutics development and manufacturing in four eras.

Therapeutic monoclonal antibody (mAb) development and the processes for manufacturing drug substance have evolved since the first approval of the mAb in 1986. As the past is often the prologue to the future, the history of these technologies has been classified here into three eras, leading to speculation about what the next era may hold with regard to development and manufacturing strategies, as well as the potential impacts to patients. The substantial increase in production culture titers and bioreactor production volumes and the availability of large-scale contract manufacturing facilities could translate into improved global access for these therapies and an expansion of indications for therapeutic antibodies.

Increasing acceptance of AI-generated digital twins through clinical trial applications.

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study.

BACKGROUND: Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade. STUDY DESIGN AND METHOD: Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets. RESULTS: The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220). CONCLUSIONS: The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.

A Modern Curriculum for Training Scientists in Model-Informed Drug Development: Progress Report on FDA Grant to Train Regulatory Scientists.

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD) topics relevant to a non-modeling audience. Several multinational pharmaceutical companies contributed case studies illustrating the application of the MIDD approach in practice. Training videos were created and divided into several modules: introducing the MIDD landscape for drug development and regulatory science, a review of various model types used for MIDD, discussions of how models inform drug development and regulatory decisions, future goals of MIDD, and discussions on the interconnectedness of models used for MIDD. Examples and vignettes from stakeholders and thought leaders were included. These educational materials fill a gap between academic and "on the job training" for regulators, academic, and industry scientists, delivering insights and value for those performing modeling and non-modelers reviewing the output of modeling and simulation work. A total of 13 hours of video content is currently available. A small panel of FDA reviewers is currently beta-testing the learning management system (LMS). Future efforts for this MIDD training initiative will include expansion of the content via an expanded and diverse faculty, 1:1 online mentorship sessions, and eventually broader access to this resource consistent with an open science approach and curriculum. The MIDD training LMS can accommodate a diverse learning ecosystem; further development may also accommodate different audiences in the future.

Making new drugs the hard way.

A new drug can have its origin in either pharma, biotech or academia. In general, discovery scientists working in pharma and biotech are advantaged over their academic counterparts and the relative advantages and disadvantages associated are discussed in depth. Against all odds, an increasing number of important drugs have had their origins in academia. This article reports three case studies from the Liotta Research Group (LRG), which explores the special circumstances that allowed these drug development campaigns to be successful. The first involves the antiretroviral agent, emtricitabine. In this case efficient synthetic methodology, developed in the LRG, coupled with some key university and commercial sector partnerships, enabled a group of academic collaborators to discover and develop a highly effective HIV reverse transcriptase inhibitor. The second case study involves the discovery and development of the breakthrough hepatitis C drug, sofosbuvir. Based on key input from Professors Schinazi and Liotta at Emory University, scientists at the Emory startup, Pharmasset, identified the nucleoside core of the drug that would become sofosbuvir. Subsequent analysis of its phosphorylation profile by Pharmasset scientists suggested that converting it to its corresponding monophosphate prodrug would circumvent a kinase block and enable it to be an effective hepatitis C polymerase inhibitor. The third case study describes the formation of DRIVE (Drug Innovation Ventures at Emory)/EIDD (Emory Institute for Drug Development), which were created to circumvent unintended impediments for carrying out academic drug discovery and development. Although DRIVE/EIDD is a wholly-owned, not-for-profit subsidiary of Emory University, it contains many attributes that enables it to operate much more nimbly than a typical academic laboratory. With an experienced drug development team and no shareholders to distract them, DRIVE/EIDD was able to focus its attention of the development of drugs to address viral diseases of global concern. In particular, their strategy to identify and develop an antiviral agent active against multiple single-stranded RNA viruses led to molnupiravir, a broadly active, oral drug that received Emergency Use Authorization for the treatment of SARS-CoV-2 infections (i.e., COVID-19).

Maximizing insights from nonclinical safety studies in the context of rising costs and changing regulations.

The traditional paradigm of non-rodent safety assessment studies, primarily reliant on non-human primates (NHPs) and dogs, is undergoing a transformation. During the 2023 Safety Pharmacology Society Annual Meeting, scientists from leading nonclinical contract organizations discussed how traditional IND-enabling studies can benefit from employing underutilized alternative non-rodent models, such as the swine. Swine offer a cost-effective approach to drug development and share many anatomical and physiological similarities with humans. The inclusion of non-traditional species in safety assessments, coupled with advanced measurement techniques, aids in de-risking compounds early on and adapting projects to the evolving cost landscape.

An update on the novel methods for the discovery of antiseizure and antiepileptogenic medications: where are we in 2024?

INTRODUCTION: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored. AREAS COVERED: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective. EXPERT OPINION: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

Editorial: The Global Threats of Increasing Antimicrobial Resistance Require New Approaches to Drug Development, Including Molecular Antimicrobial Adjuvants.

Antimicrobial resistance and the associated morbidity and mortality from untreatable common infectious organisms is an increasing threat to global public health. In 2019, the Antimicrobial Resistance Collaborators identified that antimicrobial resistance was directly responsible for up to 1.27 million deaths worldwide and was associated with up to 4.95 million deaths, with low-income and middle-income countries being the most severely affected. In 2019, before the COVID-19 pandemic began, they predicted that antimicrobial resistance could result in 10 million deaths per year by 2050, overtaking cancer as a leading cause of death worldwide. Therefore, there is an urgent need for new approaches to antimicrobial treatment. In June 2024, the findings from researchers at the Ineos Oxford Institute for Antimicrobial Research (IOI) and the Oxford University Department of Pharmacology in the UK reported the use of a small molecule that can work alongside antibiotics to suppress the development of antimicrobial resistance in bacteria. The SOS inhibitor molecule has been called OXF-077. This editorial aims to highlight the global threats from increasing antimicrobial resistance and the urgent need for new molecules that function through novel mechanisms of action, including molecular antimicrobial adjuvants.

Glutathione dynamics in subcellular compartments and implications for drug development.

Glutathione (GSH) is a pivotal tripeptide antioxidant essential for maintaining cellular redox homeostasis and regulating diverse cellular processes. Subcellular compartmentalization of GSH underscores its multifaceted roles across various organelles including the cytosol, mitochondria, endoplasmic reticulum, and nucleus, each exhibiting distinct regulatory mechanisms. Perturbations in GSH dynamics contribute to pathophysiological conditions, emphasizing the clinical significance of understanding its intricate regulation. This review consolidates current knowledge on subcellular GSH dynamics, highlighting its implications in drug development, particularly in covalent drug design and antitumor strategies targeting intracellular GSH levels. Challenges and future directions in deciphering subcellular GSH dynamics are discussed, advocating for innovative methodologies to advance our comprehension and facilitate the development of precise therapeutic interventions based on GSH modulation.

Preclinical mitigation of 5-HT2B agonism-related cardiac valvulopathy revisited.

Cardiac valvulopathy (Cardiac Valve Disease; CVD) associated with off-target activation of the 5-hydroxytryptamine (5-HT) 2B receptor has been well recognized, but is still poorly predicted during drug development. The regulatory guidance proposes the use of 5-HT2B binding data (i.e., Ki values) and free maximum therapeutic exposure (Cmax) to calculate safety margins as a threshold of detection (>10) for eliminating the risk of drug-induced cardiac valvulopathy. In this paper, we provide additional recommendations for preclinical prediction of CVD risk based on clinical pharmacodynamic and pharmacokinetic data obtained from drugs with or without 5-HT2B receptor activation. Our investigations showed that 5-HT2B agonist affinity of molecules tested in an in vitro 5-HT2B cell-based functional assay, placed in perspective to their sustained plasma exposure (AUCs) and not to their peak plasma exposure, Cmax (i.e., maximum therapeutic exposure) provide a solid basis for interpreting 5-HT2B data, for calculating safety margins and then, accurately differentiate drugs associated with a clinical risk of CVD from those which are not (despite having some agonist 5-HT2B activity). In addition, we discuss the risk of multi-organ fibrosis linked to 5-HT2B receptor activation, often underestimated, however well reported in FAERS for 5-HT2B agonists. We believe that our recommendations have the potential to mitigate the risk for the clinical development of CVD and fibrosis.

Phage display technology and its impact in the discovery of novel protein-based drugs.

INTRODUCTION: Phage display technology is a well-established versatile in vitro display technology that has been used for over 35 years to identify peptides and antibodies for use as reagents and therapeutics, as well as exploring the diversity of alternative scaffolds as another option to conventional therapeutic antibody discovery. Such successes have been responsible for spawning a range of biotechnology companies, as well as many complementary technologies devised to expedite the drug discovery process and resolve bottlenecks in the discovery workflow. AREAS COVERED: In this perspective, the authors summarize the application of phage display for drug discovery and provide examples of protein-based drugs that have either been approved or are being developed in the clinic. The amenability of phage display to generate functional protein molecules to challenging targets and recent developments of strategies and techniques designed to harness the power of sampling diverse repertoires are highlighted. EXPERT OPINION: Phage display is now routinely combined with cutting-edge technologies to deep-mine antibody-based repertoires, peptide, or alternative scaffold libraries generating a wealth of data that can be leveraged, e.g. via artificial intelligence, to enable the potential for clinical success in the discovery and development of protein-based therapeutics.

Artificial intelligence for small molecule anticancer drug discovery.

INTRODUCTION: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED: In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION: The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

Testing dilemmas in the clinic: Lessons learned from biomarker-based drug development.

Various tests based on different biomarkers have been developed to identify the best candidates for poly(ADP-ribose) polymerase (PARP)-inhibitor therapy. However, due to the absence of harmonization regarding these complex biomarkers, along with various cutoff points and unknown spatial and temporal variations, it is difficult to define the clinical utility of each test and ensure uniformity in treatment decision-making. Here, we propose measures to align biomarker definitions and minimum analytical performance characteristics for diagnostics to ensure equitable and sustainable access to precision medicine.

Doses Evaluated in Clinical Pharmacology Studies Investigating the Effect of Intrinsic and Extrinsic Factors on PK and Safety: Case Examples from Approved Drug Development Programs.

Dose selection for investigations of intrinsic and extrinsic factors of pharmacokinetic variability as well as safety is a challenging question in the early clinical stage of drug development. The dose of an investigational product is chosen considering the compound information available to date, feasibility of the assessments, regulatory requirements, and the intent to maximize information for later regulatory submission. This review selected 37 programs as case examples of recently approved drugs to explore the doses selected with focus on studies of drug interaction, renal and hepatic impairment, food effect and concentration-QTc assessment.The review found that regulatory agencies may consider alternative approaches if justified and safe as illustrated in these examples. It is thus recommendable to use the first in human trial as an opportunity to assess QT-prolongation and drug interactions using probes or endogenous markers while maximizing the DDI potential, increasing sensitivity and ensuring safety. Early understanding of dose proportionality assists dose finding and simple and fast to conduct DDI study designs are advantageous. Single dose impairment studies despite non-proportional/time-dependent PK are often acceptability.Overall, the early understanding of the drug's safety profile is essential to ensure the safety of doses selected while preventing clinical trials with unnecessary exposure when using high doses or multiple doses. The information collected in this retrospective survey is a good reminder to tailor the early clinical program to the profile and needs of the molecule and consider regulatory opportunities to streamline the development path.

Challenges and opportunities of developing small-molecule therapies for age-related macular degeneration.

Age-related macular degeneration (AMD) is the leading cause of vision loss in senior adults. The disease can be categorized into two types: wet AMD and dry AMD. Wet AMD, also known as exudative or neovascular AMD, is less common but more severe than dry AMD and is responsible for 90% of the visual impairment caused by AMD and affects 20 million people worldwide. Current treatment options mainly involve biologics that inhibit the vascular endothelial growth factor or complement pathways. However, these treatments have limitations such as high cost, injection-related risks, and limited efficacy. Therefore, new therapeutic targets and strategies have been explored to improve the outcomes of patients with AMD. A promising approach is the use of small-molecule drugs that modulate different factors involved in AMD pathogenesis, such as tyrosine kinases and integrins. Small-molecule drugs offer advantages, such as oral administration, low cost, good penetration, and increased specificity for the treatment of wet and dry AMD. This review summarizes the current status and prospects of small-molecule drugs for the treatment of wet AMD. These advances are expected to support the development of effective and targeted treatments for patients with AMD.