A simple model to solve a complex drug toxicity problem.

Development and Validation of a Test Dose Strategy for Once-Daily i.v. Busulfan: Importance of Fixed Infusion Rate Dosing

Estimating the reliability of individual predictions is key to increasing the adoption of computational models and ‘artificial intelligence’ in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings. Among the large number of algorithms developed over the last decades to compute prediction errors, Conformal Prediction (CP) has gained increasing attention in the computational drug discovery community. A major reason for its recent popularity is the ease of interpretation of the computed prediction errors in both classification and regression tasks. For instance, at a confidence level of 90% the true value will be within the predicted confidence intervals in at least 90% of the cases. This so called validity of CPs is guaranteed by the robust mathematical foundation underlying CP. The versatility of CP relies on its minimal computational footprint, as it can be easily coupled to any machine learning algorithm at little computational cost. In this review, we summarize underlying concepts and practical applications of CP with a particular focus on virtual screening and activity modelling, and list open-source implementations of relevant software. Finally, we describe the current limitations in the field and provide a perspective on future opportunities for CP in preclinical and clinical drug discovery.

Accurate drug toxicity prediction is vital for drug discovery, but existing atom-based methods are often computationally inefficient for complex molecules. This limitation creates a need for more efficient yet powerful predictive models. We introduce FGMA, a novel framework designed to address this challenge. FGMA integrates functional groups with molecular fingerprints for a multi-view molecular encoding. The framework extracts key functional groups, treating them as unified entities to reduce computational overhead. These are then combined with MACCS fingerprints using a crossattention mechanism to capture both local chemical motifs and global structural properties. Experimental results demonstrate that FGMA achieves high accuracy and robustness in toxicity prediction tasks. In addition, interpretability analyses successfully identified how specific structural features and functional groups within drug molecules contribute to toxicity outcomes. Our findings validate that combining functional groups with fingerprints is an effective strategy that balances computational efficiency and representational power. The model's interpretability offers valuable, actionable insights into structure-toxicity relationships, aiding in the design of safer compounds. FGMA offers an effective, efficient, and interpretable tool for drug toxicity analysis. It stands to accelerate the drug discovery pipeline by enabling faster and more insightful safety assessments of potential drug candidates.

The greater development in the computational tools and the presence of a huge number of established chemical libraries emerge the inquiry about the ability to integrate the computational techniques in the drug discovery process. This review emphasizes the role of computational tools and software to accelerate the steps in the drug discovery pipeline. The data were gathered from the trendiest research articles and reviews that were indexed in the well-established scholarly search engines. Different vital techniques, such as virtual screening, pharmacophore modeling, molecular docking, molecular dynamic simulations, and quantitative structure activity relationship, are widely employed for target recognition, lead refining, and forecasting binding behavior at the atomic level. The advancement in artificial cognitive computing software significantly expanded the capabilities for rapid analysis of substantial data sets and the generation of toxicity and bioactivity predictive models of drugs. The contributions of these software tools tackled global problems, like drug repurposing during the coronavirus outbreak, and the direction toward personalized medicaments highlight their crucial role in the swift discovery of new treatments. In addition, computational tools are widely employed to enhance the drug formulations, which leads to discovering drugs with optimum pharmacokinetic and toxicity profiles. The difficulties in analyzing the multi-target binding behaviors and the limited success rates of the generated candidates in the experimental validations were the main existing limitations. However, the steep development in the field of artificial intelligence and the hybrid biochemical-computational approaches provided promising horizons to tackle these limitations. Chemical computational tools significantly affect the future of pharmaceutical research by boosting the drug discovery process toward affordable and efficient medicaments.

Therapeutic drug monitoring (TDM), an established practice for many antimicrobials, has recently seen increasing utilization in the management of patients receiving antifungal agents. There is a growing body of literature supporting the use of TDM for itraconazole, voriconazole, posaconazole, and flucytosine. In addition, clinical practice guidelines have been recently published that give recommendations on the appropriate use of TDM for antifungal agents. However, there are still uncertainties regarding the optimal use of antifungal TDM in clinical practice. We conducted a review of recent literature in order to describe the clinical situations and specific antifungal agents for which TDM is ideal and summarize key information about the pharmacokinetics, pharmacodynamics, drug toxicities, TDM concentration targets, and dose adjustment algorithms for antifungals in which routine TDM is performed.

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Ligand Specific Efficiency (LSE) Index for PET Tracer Optimization.

A number of alternative variables have appeared in the medicinal chemistry literature trying to provide a more rigorous formulation of the guidelines proposed by Lipinski to exclude chemical entities with poor pharmacokinetic properties early in the discovery process. Typically, these variables combine the affinity towards the target with physicochemical properties of the ligand and are named efficiencies or ligand efficiencies. Several formulations have been defined and used by different laboratories with different degrees of success. A unified formulation, ligand efficiency indices, was proposed that included efficiency in two complementary variables (i.e., size and polarity) to map and monitor the drug-discovery process (AtlasCBS). The use of this formulation in combination with an extended multiparameter optimization is presented, with examples, as a promising methodology to optimize the drug-discovery process in the future. Future perspectives and challenges for this approach are also discussed.

Therapeutic drug monitoring (TDM) is a precision medicine tool that measures drug concentrations in biological fluids to guide individualized dosing. TDM has evolved into a multidisciplinary approach used across various therapeutic areas. In India, despite a growing clinical need, TDM remains underutilized due to systemic and infrastructural barriers. This narrative review aims to summarize the evolution, current status, analytical approaches, and future prospects of TDM in India and is based on published literature retrieved from databases, including PubMed, Scopus, and Google Scholar, and outlines TDM's utility in optimizing pharmacotherapy for drugs with narrow therapeutic windows or nonlinear pharmacokinetics. It compares analytical methods used for TDM and discusses challenges in the workflow of TDM. Special emphasis is placed on its relevance in children, pregnant women, the elderly, and critically ill patients. It also highlights barriers, including a lack of infrastructure, trained personnel, and standardized guidelines. TDM in India requires policy-level support, integration into national health programs, clinician education, and adoption of point-of-care technologies. Strengthening research in pharmacogenetics can help establish TDM as a cornerstone of individualized therapy.

Disease-processes experienced by critically ill patients commonly cause unique pharmacokinetic (PK) changes, that may result in drug concentrations that are either sub-therapeutic or toxic when dosing is based on PK data obtained from non-critically ill patients. Ensuring therapeutic dosing of beta-lactam antibiotics in critically ill patient is of increasing concern, given the difficult-to-predict PK in this population, frequent use of this antibiotic class in treating severe infections and the importance of optimal antibiotic therapy for reducing mortality rates. Therapeutic drug monitoring (TDM), a strategy frequently used in dosing drugs with complex PK, has demonstrated potential clinical advantages for improving dosing of beta-lactams in the critically ill. However, the practice of beta-lactam TDM and dose optimisation is still limited by a number of factors. Large-scale data on the adequacy of current dosing regimens and optimal PK/pharmacodynamic (PK/PD) targets associated with maximal clinical outcome is still limited. In particular, currently available data on beta-lactam TDM have not adequately quantified the potentially profound impact of hypoalbuminaemia, common among the critically ill, on the PK of the pharmacologically active unbound concentration of beta-lactams.This Thesis aims to describe the application of a beta-lactam TDM-guided dosing program in critically ill patients, with the utilization of directly-measured unbound drug concentration data. Specifically, this work will first characterise the impact of altered protein binding in critically ill patients on the accuracy of commonly used methods for calculation of unbound beta-lactam concentrations (from measured total concentrations). Secondly, this work will describe the achievement of PK/PD targets in relation to directly-measured unbound beta-lactam concentrations in critically ill patients receiving empiric dosed and TDM-guided beta-lactam antibiotic treatments. Finally, the association of beta-lactam concentrations with clinical outcome in critically ill patients with blood stream infections will also be characterised.This Thesis comprises of seven chapters. Chapter one introduces relevant PK/PD and beta-lactam dosing concepts for this Thesis.Chapter two includes a published review article that systemically analyses the current literature at the time on the risk of inadequate drug exposure in critically ill patients and discusses the role of TDM for different class of antibiotics in this setting. This chapter specifically addresses available evidence relating to practices of antibiotic TDM, and the practical aspects of applying TDM to potentially improve outcomes from severe infections in the critically ill.Chapter three is an original research article that reports findings of a multicentre survey on the various approaches used for beta-lactam TDM in intensive care units (ICUs) worldwide.Chapter four is an original research article that describes findings of a single-centre prospective study on unbound beta-lactam concentrations in the critically ill determined by two assays. The aim of this study was to compare the measured unbound concentrations with the unbound concentrations conventionally predicted from published protein binding values for seven beta-lactam antibiotics commonly used in the critically ill. Chapter five describes findings of a single-centre prospective study on attainment of predefined PK/PD targets in critically ill patients who received empirically dosed and TDM-guide (using direct-measured unbound drug concentrations) beta-lactam antibiotics. Furthermore, the study also aimed to describe patient and treatment factors associated with attainment of target concentrations.Chapter six describes findings of association between beta-lactam exposures, using PK/PD indices, and clinical outcomes from a retrospective analysis of prospective studies database. Furthermore, the study also aimed to describe patient and treatment factors associated with patient outcome.Chapter seven includes an overall discussion and summary of the findings of this Thesis, and explores on potential future research in this area.

Accelerating antischistosomal drug discovery : preclinical studies of antimalarials, synthetic peroxides and praziquantel derivatives

Therapeutic Dose Monitoring of Busulfan Is Associated with Reduced Risk of Relapse in Non-Hodgkin Lymphoma Patients Undergoing Autologous Stem Cell Transplantation.

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

Correlation between Targeted Protein and Drug Side Effects: A Step towards the Prediction of Drug Toxicity

Eltrombopag is an oral thrombopoietin receptor agonist approved for the treatment of patients with chronic idiopathic thrombocytopenic purpura (ITP), who are more than 1 year old, and show poor response to first-line therapy. ITP is a hematological disorder characterized by isolated thrombocytopenia in the absence of secondary causes or disorders. Eltrombopag is generally well tolerated in the pediatric population; therefore, therapeutic drug monitoring (TDM) is not usually performed in clinical practice.We presented the case study of a 3-year-old girl with chronic ITP. She arrived in the pediatric intensive care unit with acute liver failure due to eltrombopag toxicity despite taking the standard drug dosage. A very high eltrombopag plasma concentration, indicating drug toxicity, was found through TDM. The patient also carried the allelic variations that are involved in drug metabolism [CYP2C8 and UDP glucuronosyltransferase (UGT) 1A1 (UGT1A1)] and drug cellular transportation [ABCG2 (ATP-binding cassette G2)]. This observation highlights the importance of using TDM and pharmacogenetic approaches to manage patients' unusual complications associated with standard pharmacological treatment regimens.