Re-evaluating acceptable risk of death from gene therapy: A threshold study among individuals with Duchenne muscular dystrophy and their caregivers in the US and UK.

The ever-increasing costs of in vitro and in vivo testing are compelling scientists to increasingly rely on computational models for predictive characterisation at early stages of drug discovery and development. The complexity of this stage requires high-throughput screening methods that can rapidly generate comprehensive information about new chemical compounds. This review explores innovative approaches assessing pharmacokinetic and pharmacodynamic properties of new chemical entities, with a focus on integrating machine learning as a transformative analytical tool. Machine learning algorithms are highlighted for their capability to train sufficient predictors combining biomimetic chromatography data (a high-throughput alternative for several physicochemical assays) with molecular features and/or molecular fingerprints obtained in silico and in vivo data of known compounds to allow efficient prediction of in vivo data for new chemical entities. By synthesising recent methodological advancements and giving useful practical approaches, the review provides insights into computational strategies that can significantly accelerate compound library screening and drug development processes.

Several pieces of evidence have demonstrated the sigma-1 receptor (S1R) as a druggable protein with important therapeutic potentials, including neurodegeneration, cancer, and neuropathic pain. The density of S1R is altered in pathological processes so that its imaging is under study for diagnostic purposes. Thus, research has been focused on the development of S1R positron emission tomography (PET) radioligands, not only as diagnostic tools but also as powerful means to assist in the drug-development process. Herein, we comprehensively review the most important S1R PET radiotracers belonging to different classes that have been developed in the last two decades. Starting from the structural modifications impacting on the S1R affinity and selectivity, we report (i) the differences in metabolism and pharmacokinetics, (ii) the in vivo behavior in different animal models, (iii) the in vitro autoradiography outcomes, and (iv) the dosimetric profiles. The successful use of the best-performing S1R PET radiotracers in the characterization of novel S1R drugs is also reported together with the approaches to assess the potential for clinical translation. What emerges from this review is that, although the development of reliable PET agents appears to be extremely challenging, these radiotracers hold incredible potential and play a fundamental role in the exploitation of S1R in health and disease.

This paper investigates an enzymatic reaction model formulated with the Atangana–Baleanu–Caputo (ABC) fractional derivative, aiming to enhance the classical description of enzyme kinetics. Existence and uniqueness of the solution are established through nonlinear functional analysis, while approximate solutions are obtained via the Laplace Adomian decomposition method (LADM). To validate and complement the numerical scheme, we employ a neural network framework that demonstrates the capability of intelligent computing to approximate fractional biochemical dynamics. Sensitivity analysis is carried out, and numerical simulations are illustrated through 2D and 3D plots. The study highlights how the combination of fractional calculus and neu-ral networks offers new perspectives for modelling enzyme kinetics. The results indicate potential applications in drug development, metabolic engineering, and biochemical process optimization, providing a pathway for more precise control strategies in biochemical systems.

In the digital era, the integration of computational methods in drug discovery has revolutionized laboratory practices, enhancing efficiency and accuracy in drug development. This article provides a comprehensive review of the crucial role of computational technology in accelerating the discovery of new drug compounds, highlighting its impact on the effectiveness and success of pharmaceutical development. Traditional drug discovery methods often require extensive time and high costs, with relatively low success rates in clinical trials. To address these challenges, various computational approaches, such as Molecular Docking, Quantitative Structure-Activity Relationship (QSAR), and machine learning, have been widely adopted in the pharmaceutical industry. This study employs a systematic approach to explore different computational techniques and their applications in identifying potential drug candidates. Findings indicate that computational tools significantly expedite the drug development process, reduce costs, and improve the success rates of clinical trials. The conclusion emphasizes the importance of leveraging computational technology as an innovative strategy in pharmaceutical research and development, ultimately accelerating the discovery of safer and more effective therapies. Keywords: Computation, Drug Discovery, Digital Era, QSAR, Molecular Docking.

The value proposition in clinical trials: a framework for drug development.

Mass spectrometric analysis of the amiodarone-induced alteration of phosphatidylcholines in living single cells.

Harnessing AI for precision medicine and its applications in genomics, systems pharmacology, and drug discovery.

Artificial intelligence (AI) is transforming pharmacology, drug safety, and toxicology by accelerating the drug development process to be more efficient, precise, and economical. Conventional drug discovery, pre-clinical testing, and post-marketing surveillance methods frequently encounter high costs, long lead times, ethical constraints, and low predictive validity in human outcomes. Utilizing machine learning (ML) and deep learning (DL), AI combines heterogenous datasets chemical structures, genomics, clinical data, and imaging to bridge these gaps.In drug design and discovery, AI has hastened predictions of protein and RNA structures (e.g., AlphaFold), enhanced virtual screening, and enabled de novo drug design with generative models. It has also hastened peptide-based drug development and improved pharmacokinetic prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) and reduced failure rates.

Quantitative Systems Pharmacology (QSP) is a powerful approach to provide decision-making support throughout the drug development process. QSP comes with many challenges in model development, validation, and applications. Traditional QSP workflows are limited by slow knowledge integration, labor-intensive model construction, inconsistent validation practices, and restricted scalability. In this work, we introduce QSP-Copilot, the first end-to-end AI-augmented solution designed to improve QSP modeling workflows by integrating a multi-agent system utilizing large language models (LLMs). QSP-Copilot provides modular support from project scoping and model structuring to model evaluation and reporting. Through the automation of routine tasks, QSP-Copilot reduces model development time by approximately 40% and improves methodological transparency through systematic documentation of literature sources and modeling assumptions. We demonstrate QSP-Copilot's application for two rare diseases of blood coagulation and Gaucher disease. In the blood coagulation case, automated extraction from ten peer-reviewed articles yielded 179 biological entity interaction pairs; out of these, only 105 unique mechanisms were retained after standardization. For Gaucher disease, screening nine articles produced 151 pairs, which were consolidated into 68 distinct biological interactions following the same post-processing workflow. The extraction precision for blood coagulation and Gaucher disease is 99.1% and 100.0%, respectively. QSP-Copilot extractions can be incorporated into effect diagrams with minimal expert filtering, significantly reducing the manual curation burden. The integration of AI-augmented workflows like QSP-Copilot represents a pivotal shift toward enhanced scalability and impact for QSP across the drug development pipelines, especially in disease areas where biological knowledge is sparse, such as rare diseases.

Patients often have suboptimal understanding of informed consent in clinical trials, impeding their ability to make informed decisions about participation. Additionally, translating complex informed consent information from English into other languages can introduce new areas of misunderstanding for patients. This qualitative study examined how English- and Spanish-speakers understood and perceived a complex clinical informed consent form. We tested an informed consent form for a clinical trial on a gynecological medication with women who represented the study population. We conducted 18 semi-structured interviews with English- (n = 9) and Spanish-speaking (n = 9) participants to explore areas of misunderstanding, concerns, and preferences. With Spanish-speakers, we tested two professionally translated versions of the informed consent form-one generated by general translators and the second by translators with medical and scientific expertise. We used thematic analysis to explore patterns in the data. Five themes were common across both English- and Spanish-speakers: difficulty with medical jargon; unfamiliarity with the drug development and testing process; desire for understandable numeric information; aversion to uncertain or conflicting evidence; and affective reactions to information. Spanish-speakers generally preferred language from the medical translation over the general version, though this preference was not consistent. Findings underscore the importance of pretesting informed consent materials to ensure that they are understandable, avoid difficult language, and do not evoke negative emotions or reactions, particularly when communicating about risk and uncertainty. Findings also suggest using a combination of translation approaches, when resources allow, and reinforce the value of using plain language familiar to audiences.

Artificial intelligence (AI) is being actively integrated into the drug development process, significantly accelerating it and reducing the costs of creating new therapeutic agents. This article examines key areas of AI application in the pharmaceutical industry, including molecular structure prediction, virtual compound screening, optimization of clinical trials, and personalized medicine. Special attention is paid to the ethical issues arising from the use of AI, such as algorithm transparency, accountability for errors, data privacy, technology accessibility, and regulatory challenges. The World Health Organization (WHO) guidelines for managing large multi-modal models in healthcare are analyzed. The article emphasizes the need to strike a balance between innovation and ethical responsibility, as well as to develop a regulatory framework for the safe and effective use of AI in medicine.

AI comprises well-established technology for learning and identifying novel features, such as machine learning. The present article first discusses an overview of drug research, design, and development. We have also entrusted collaborations with pharmaceutical companies and artificial intelligence companies in drug development. Artificial Intelligence is primarily driven by neural net-works, namely deep neural networks (DNN) and recurrent neural networks (RNN). There are many different AI algorithms; this article describes the most widely used algorithms in the field. In recent years, the process of drug development has been encouraged by artificial Intelligence (AI). The article also provides examples of how AI and ML are being used to treat incurable diseases like cancer. A promising novel chemical found during drug discovery must proceed through the difficult and drawn-out drug development procedure; artificial intelligence techniques are being used more and more in drug discovery to deal with problems that have proven difficult to resolve.

Background A number of patient organizations have recently embraced venture philanthropy, a model of patient advocacy that purports to use practices from venture capitalism in pursuit of philanthropic goals. However, a clear understanding of what venture philanthropy entails and what these organizations do remains elusive, hindering efforts to assess ethical implications of the model’s growth. Methods We conducted a qualitative content analysis of self-reported profiles of 130 organizations in an affinity network promoting principles of venture philanthropy. We analyzed organizations’ research goals, funding strategies, activities, and patient engagement efforts. Results Despite finding substantial variation in age, revenue, and disease focus, we identified shared assumptions and approaches that represent defining characteristics of venture philanthropy. First, organizations consistently present facilitating the development of new therapies as the most urgent need for patients. Second, organizations participate in financing and managing research across the development pipeline, rather than focusing on basic research as many patient organizations historically have done. Third, organizations seek to position themselves within established research and drug development networks, fostering collaborative relationships with key stakeholders, including pharmaceutical companies. We also find that some of the most transformative practices associated with venture philanthropy, such as direct investment in for-profit companies, remain relatively uncommon. Conclusions Venture philanthropy represents an evolution in the ambitions and activities of patient organizations, with organizations becoming more fully enmeshed in the drug development process. Our findings raise ethical questions about how patient organizations conceptualize and advance patient interests and about tradeoffs inherent to the venture philanthropy model.

Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.

This study explores the role of medicinal chemistry in the entire drug development process, from molecular design to post-marketing monitoring. Using a literature study method with a descriptive qualitative approach, data were collected from various scientific journals published between 2020 and 2025 that are relevant to drug development and the application of medicinal chemistry. The findings indicate that medicinal chemistry plays a crucial role in designing, modifying, and optimizing the structure of bioactive compounds through approaches such as structure–activity relationship (SAR) analysis, in silico modeling, and modern biotechnology. These approaches enable the discovery of new compounds that are more selective, effective, and safe. Examples include natural compounds such as betel leaf (Piper betle), green tea, berberine, and curcumin derivatives, which show potential as anticancer, antiviral, and antimalarial drug candidates. In addition, biotechnology-based therapies such as trastuzumab highlight the success of medicinal chemistry in the development of targeted therapies. However, the translation process from laboratory research to clinical application still faces several challenges, including high research costs, preclinical data reproducibility issues, limited infrastructure, and strict regulatory frameworks. Therefore, multidisciplinary collaboration, methodological innovation, and supportive policy development are required to strengthen the translation of research outcomes. Overall, medicinal chemistry plays a strategic role in accelerating the discovery of new drugs that are safe, effective, and globally competitive. The findings also show that integrating traditional approaches with modern computational methods and multidisciplinary collaboration can further accelerate new drug discovery.

Modern computational screening methods are valuable tools for repurposing approved drugs for novel therapeutic applications. They provide initial insights into alternative uses and may significantly shorten the lengthy process of drug development and regulatory approval. Treatment options for glycosphingolipidoses, lysosomal storage diseases involving glycosphingolipids (GSLs), are currently limited to a few drugs that inhibit de novo GSL biosynthesis, such as eliglustat and miglustat (Zavesca®). In the search for alternative drugs, dapagliflozin emerged as a promising candidate for off-target therapy. In the present study, we investigated whether dapagliflozin can indeed inhibit GSL synthesis, as predicted by previous computational analyses, and compared its effects with those of the glycosphingolipid synthesis inhibitor, the eliglustat analog Genz-123346, in murine 3T3 and Hepa 1-6 cell lines. While Genz-123346 significantly inhibited glycosphingolipid biosynthesis at concentrations as low as 1 µM, dapagliflozin, even up to 50 µM, had no effect on biosynthesis or de novo biosynthesis in either cell line. These results indicate that dapagliflozin, although assessing effects on the cell cycle, including proliferation at high concentrations, is not a suitable candidate for treating glycosphingolipid storage diseases by substrate reduction.

Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy—using more than one drug for treatment—can enhance efficacy and minimize the side effects associated with individual drugs. However, there is currently limited research focused on computational approaches to combination therapy for viral diseases. This paper proposes AI-based models to predict novel drug combinations that can synergistically treat viral diseases. To achieve this, we have compiled a comprehensive dataset containing information on viruses, drug compounds, and their approved interactions. We introduce two attention-based models and compare their performance with traditional machine learning and deep learning models in predicting synergistic drug pairs for treating viral diseases. Among all the methods tested, the random forest algorithm and one of the attention-based models utilizing a customized dot product as a predictor showed the highest performance. Notably, two predicted combinations—acyclovir + ribavirin and acyclovir + Pranobex Inosine—have been experimentally validated to produce a synergistic antiviral effect against the herpes simplex virus type 1, as reported in existing literature.

Our study presents DeepMice, a novel artificial intelligence-based molecular docking framework designed to predict protein-ligand binding conformations with improved accuracy. DeepMice's scoring function utilizes a graph transformer network (GTN) as its backbone. It transforms residue-level representations into atomic-level representations, enhancing representation precision. A multilevel mapping module is incorporated to reduce the graph model's size and computational complexity. Subsequently, the mixture density network (MDN) is employed to further realize scoring prediction. In terms of conformational search, DeepMice employs a hybrid strategy combining global heuristic search and local gradient-based optimization. The process initiates with a global exploration using the Differential Evolution (DE) algorithm, followed by local refinement via the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. This combined approach enhances conformational search efficiency. Performance tests on the DEKOIS2.0 and DUD-E datasets showed that DeepMice outperformed existing virtual screening technologies such as Glide SP and RTMScore in terms of area under the receiver operating characteristic curve (AUROC), boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and enrichment factor (EF) values. In particular, DeepMice demonstrates advanced molecular docking capabilities in the CASF-2016 standard test set. In addition, DeepMice considers the multiscale structure of proteins, optimizing the conformation scoring process and improving docking efficiency. In summary, DeepMice is an efficient and accurate molecular docking model, which is expected to accelerate the process of new drug research and development. The program based on the DeepMice model, which is now freely available at https://www.deepmice.com , provides a powerful tool for drug discovery.

Focal adhesion kinase inhibitors in fibrotic diseases therapy: Development and therapeutic potential.