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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.

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Innately designed to induce physiological changes, pharmaceuticals are foreknowingly hazardous to the ecosystem. Advanced oxidation processes (AOPs) are recognized as a set of contemporary and highly efficient methods being used as a contrivance for the removal of pharmaceutical residues. Since reactive oxygen species (ROS) are formed in these processes to interact and contribute directly toward the oxidation of target contaminant(s), a profound insight regarding the mechanisms of ROS leading to the degradation of pharmaceuticals is fundamentally significant. The conceptualization of some specific reaction mechanisms allows the design of an effective and safe degradation process that can empirically reduce the environmental impact of the micropollutants. This review mainly deliberates the mechanistic reaction pathways for ROS-mediated degradation of pharmaceuticals often leading to complete mineralization, with a focus on acetaminophen as a drug waste model.

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The oral route stands out as the most commonly used method for drug administration, prized for its non-invasive nature, patient compliance, and easy administration. Several elements influence the absorption of oral medications, including their solubility, permeability across mucosal membranes, and stability within the gastrointestinal (GI) environment. Research has delved into comprehending physicochemical, biochemical, metabolic, and biological obstacles that impact the bioavailability of a drug. To improve oral drug absorption, several pharmaceutical technologies and delivery methods have been studied, including cyclodextrins, micelles, nanocarriers, and lipid-based carriers. This review examines both traditional and innovative drug delivery methods, as well as the physiological and pharmacological barriers influencing medication bioavailability when taken orally. Additionally, it describes the challenges and advancements in developing formulations suitable for oral use.

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The buccal cavity, also known as the oral cavity, is a complex anatomical structure that plays a crucial role in various physiological processes. It serves as a gateway to the digestive system and facilitates the initial stages of food digestion and absorption. However, its significance extends beyond mere digestion as it presents a promising route for drug delivery, particularly to the brain. Transferosomes are lipid-based vesicles that have gained significant attention in the field of drug delivery due to their unique structure and properties. These vesicles are composed of phospholipids that form bilayer structures capable of encapsulating both hydrophilic and lipophilic drugs. Strategies for the development of buccal transferosomes for brain delivery have emerged as promising avenues for pharmaceutical research. This review aims to explore the various approaches and challenges associated with harnessing the potential of buccal transferosomes as a means of enhancing drug delivery to the brain. By understanding the structure and function of both buccal tissue and transferosomes, researchers can develop effective formulation methods and characterization techniques to optimize drug delivery. Furthermore, strategic approaches and success stories in buccal transferosome development are highlighted, showcasing inspiring examples that demonstrate their potential to revolutionize brain delivery.

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Cytochrome P450 enzymes (P450s) play a critical role in drug metabolism, with the CYP3A subfamily being responsible for the biotransformation of over 50% of marked drugs. While CYP3A enzymes are known for their extensive catalytic versatility, one intriguing and less understood function is the ability to mediate carbon-carbon (C-C) bond cleavage. These uncommon reactions can lead to unusual metabolites and potentially influence drug safety and efficacy. This review focuses on examining examples of C-C bond cleavage catalyzed by CYP3A, exploring the mechanisms, physiological significance, and implications for drug metabolism. Additionally, examples of CYP3A-mediated ring expansion via C-C bond cleavages are included in this review. This work will enhance our understanding of CYP3A-catalyzed C-C bond cleavages and their mechanisms by carefully examining and analyzing these case studies. It may also guide future research in drug metabolism and drug design, improving drug safety and efficacy in clinical practice.

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Drug overuse harms the biosphere, leading to disturbances in ecosystems' functioning. Consequently, more and more actions are being taken to minimise the harmful impact of xenopharmaceuticals on the environment. One of the innovative solutions is using biosorbents-natural materials such as cells or biopolymers-to remove environmental pollutants; however, this focuses mainly on the removal of metal ions and colourants. Therefore, this study investigated the biosorption ability of selected pharmaceuticals-paracetamol, diclofenac, and ibuprofen-by the biomass of the cyanobacteria Anabaena sp. and Chroococcidiopsis thermalis, using the LC-MS/MS technique. The viability of the cyanobacteria was assessed by determining photosynthetic pigments in cells using a UV-VIS spectrophotometer. The results indicate that both tested species can be effective biosorbents for paracetamol and diclofenac. At the same time, the tested compounds did not have a toxic effect on the tested cyanobacterial species and, in some cases, stimulated their cell growth. Furthermore, the Anabaena sp. can effectively biotransform DCF into its dimer.

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Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns. The algorithms must be broadly applicable to enable reliable predictions, even in regions of the protein or molecule spaces where data may be scarce. In this paper, we address two key challenges to fulfill these goals: building large, high-quality training datasets and designing prediction methods that can scale, in order to be trained on such large datasets. First, we introduce LCIdb, a curated, large-sized dataset of DTIs, offering extensive coverage of both the molecule and druggable protein spaces. Notably, LCIdb contains a much higher number of molecules than publicly available benchmarks, expanding coverage of the molecule space. Second, we propose Komet (Kronecker Optimized METhod), a DTI prediction pipeline designed for scalability without compromising performance. Komet leverages a three-step framework, incorporating efficient computation choices tailored for large datasets and involving the Nyström approximation. Specifically, Komet employs a Kronecker interaction module for (molecule, protein) pairs, which efficiently captures determinants in DTIs, and whose structure allows for reduced computational complexity and quasi-Newton optimization, ensuring that the model can handle large training sets, without compromising on performance. Our method is implemented in open-source software, leveraging GPU parallel computation for efficiency. We demonstrate the interest of our pipeline on various datasets, showing that Komet displays superior scalability and prediction performance compared to state-of-the-art deep learning approaches. Additionally, we illustrate the generalization properties of Komet by showing its performance on an external dataset, and on the publicly available LH benchmark designed for scaffold hopping problems. Komet is available open source at https://komet.readthedocs.io and all datasets, including LCIdb, can be found at https://zenodo.org/records/10731712.

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Deconvolution and convolution are powerful tools that allow decomposition and reconstruction, respectively, of plasma versus time profiles from input and impulse functions. While deconvolution have commonly used compartmental approaches (e.g., Wagner-Nelson or Loo-Riegelman), convolution most typically used the convolution integral which can be solved with numerical methods. In 2005, an analytical solution for one-compartment pharmacokinetic was proposed and has been widely used ever since. However, to the best of our knowledge, analytical solutions for drugs distributed in more than one compartment have not been reported yet. In this paper, analytical solutions for compartmental convolution from both original and exact Loo-Riegelman approaches were developed and evaluated for different scenarios. While convolution from original approach was slightly more precise than that from the exact Loo-Riegelman, both methods were extremely accurate for reconstruction of plasma profiles after respective deconvolutions. Nonetheless, convolution from exact Loo-Riegelman was easier to interpret and to be manipulated mathematically. In fact, convolution solutions for three and more compartments can be easily written with this approach. Finally, our convolution analytical solution was applied to predict the failure in bioequivalence for levonorgestrel, demonstrating that equations in this paper may be useful tools for pharmaceutical scientists.

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Drug delivery is an important factor for the success of ocular drug treatment. However, several physical, biochemical, and flow-related barriers limit drug exposure of anterior and posterior ocular target tissues during drug treatment via topical, subconjunctival, intravitreal, or systemic routes. Mathematical models encompass various barriers so that their joint influence on pharmacokinetics (PKs) can be simulated in an integrated fashion. The models are useful in predicting PKs and even pharmacodynamics (PDs) of administered drugs thereby fostering development of new drug molecules and drug delivery systems. Furthermore, the models are potentially useful in interspecies translation and probing of disease effects on PKs. In this review article, we introduce current modeling methods (noncompartmental analyses, compartmental and physiologically based PK models, and finite element models) in ocular PKs and related drug delivery. The roles of top-down models and bottom-up simulations are discussed. Furthermore, we present some future challenges, such as modeling of intra-tissue distribution, prediction of drug responses, quantitative systems pharmacology, and possibilities of artificial intelligence.

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Stress testing (also known as forced degradation) of pharmaceutical drug substances and products is a critical part of the drug development process, providing insight into the degradation pathways of drug substances and drug products. This information is used to support the development of stability-indicating methods (SIMs) capable of detecting pharmaceutically relevant degradation products that might potentially be observed during manufacturing, long-term storage, distribution, and use. Assessing mass balance of stressed samples is a key aspect of developing SIMs and is a regulatory expectation. However, the approaches to measure, calculate, and interpret mass balance can vary among different pharmaceutical companies. Such disparities also pose difficulties for health authorities when reviewing mass balance assessments, which may result in the potential delay of drug application approvals. The authors have gathered input from 10 pharma companies to map out a practical review of science-based approaches and technical details to assess and interpret mass balance results. Key concepts of mass balance are introduced, various mass balance calculations are demonstrated, and recommendations on how to investigate poor mass balance results are presented using real-world case studies. Herein we provide a single source reference on the topic of mass balance in pharmaceutical forced degradation for small molecule drug substances and drug products in support of regulatory submissions with the goal of facilitating a shared understanding among pharmaceutical scientists and health authorities.

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Aim: The aim was to evaluate drug-plasma binding (DPB).by employing Hollow Fiber-in-Syringe Equilibrium Sampling Through Supported Liquid Membrane (HFiS ESTSLM) and RP-HPLC analysis.Materials & Methods: HFiS ESTSLM and RP-HPLC were used to evaluate DPB of three weak basic drugs (Metoprolol, Diphenhydramine, and Sildenafil) with differing hydrophilicity and binding ability to blood plasma.Results: The results exhibited an increasing drug-dependent magnitude of DPB for the three model drugs. This trend of DPB confirmed that HFiS ESTSLM has the required sensitivity for determining DPB of the drugs. The DPB was drug concentration-dependent within the tested drug concentration range, especially at high concentration.Conclusion: HFiS ESTSLM and RP-HPLC offered a simple, easy and cost-effective procedure to evaluate DPB of these basic drugs.

[Box: see text].

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To date, the presence of pharmaceuticals has been extensively documented across a wide range of aquatic systems and biota. Further, substantial progress has been made in transitioning from laboratory assessments of pharmaceutical fate and effects in fish to in situ assessments of exposure and effects; however, certain research areas remain understudied. Among these is investigation of differential accumulation across multiple internal tissues in wild marine fish beyond the species commonly sampled in laboratory and freshwater field settings. This study examined the presence of pharmaceuticals across four tissues (plasma, muscle, brain, and liver) in a wild marine fish, bonefish (Albula vulpes), throughout coastal South Florida, USA. Differential accumulation across tissues was assessed for the number and concentration, identity, and composition of accumulated pharmaceuticals by sampling 25 bonefish and analyzing them for 91 pharmaceuticals. The concentration of pharmaceuticals was highest in plasma > liver > brain > muscle, while the number of pharmaceuticals was highest in liver > brain > plasma > muscle. The identity of detected pharmaceuticals was tissue specific, and there was an inverse relationship between the number of detections for each pharmaceutical and its log Kow. The composition of pharmaceuticals was tissue specific for both pharmaceutical presence/absence and concentration. Across all tissues, the greatest similarity was between brain and liver, which were more similar to plasma than to muscle, and muscle was the most distinct tissue. For tissue compositional variability, muscle was the most diverse in accumulated pharmaceuticals, while plasma, brain, and liver were similarly variable. With the highest concentrations in plasma and highest number in liver, and documented variability in accumulated pharmaceuticals across tissues, our results highlight the importance of tissue selection when surveying exposure in wild fish, suggesting that multi-tissue analysis would allow for a more comprehensive assessment of exposure diversity and risk of adverse effects.

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Drug labeling and instructions provide essential information for patients regarding the usage of drugs. Instructions for the dosage of drug usage are critical for the effectiveness of the drug and the safety of patients. The dosage of many drugs varies depending on the patient's age. However, as our understanding of human biology deepens, we believe that these instructions need to be modified to incorporate different life stages. This is because human biology and metabolism differ significantly among different life stages, and their responses to drugs also vary. Additionally, the same age of different persons may fall into different life stages. Therefore, our group from multiple institutes and countries proposes a reexamination of whether incorporating life stages in all or any drug instructions will greatly enhance drug efficiency and patients' health.

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This comprehensive review elucidates the pivotal role of microbes in drug metabolism, synthesizing insights from an exhaustive analysis of over two hundred papers. Employing a structural classification system grounded in drug atom involvement, the review categorizes the microbiome-mediated drug-metabolizing capabilities of over 80 drugs. Additionally, it compiles pharmacodynamic and enzymatic details related to these reactions, striving to include information on encoding genes and specific involved microorganisms. Bridging biochemistry, pharmacology, genetics, and microbiology, this review not only serves to consolidate diverse research fields but also highlights the potential impact of microbial drug metabolism on future drug design and in silico studies. With a visionary outlook, it also lays the groundwork for personalized medicine interventions, emphasizing the importance of interdisciplinary collaboration for advancing drug development and enhancing therapeutic strategies.

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Chirality has an important role in the drug design because enantiomers may exhibit different bioactivity when interacting with macromolecules of a living organism. In our previous work, based on the analysis of a set of 100 chiral drugs, a relationship was established between the sign of chirality of enantiomers and their bioactivity. To understand the reasons for the observed patterns of chiral specificity of drug enantiomers, the interaction of 10 enantiomeric pairs of chiral drugs with the corresponding target proteins has been considered using molecular docking and further postprocessing by quantum chemistry methods. The data obtained confirm that the energetic aspect of the interaction between opposite enantiomers and target protein affects the enantiomer biological activity. In addition, the results show that molecular docking is able to distinguish between bioactive and inactive/less active enantiomers, although many docking programs are not accurate enough to distinguish a weak inhibitor from a strong one.

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This review considers the role of in vitro permeation testing (IVPT) for the evaluation of drug delivery from topical formulations applied to the skin. The technique was pioneered by Franz in the 1970's and today remains an important tool in the development, testing and optimization of such topical formulations. An overview of IVPT as well as selection of skin for the experiment, integrity testing of the membrane, and required number of replicate skin samples is discussed. In the literature many researchers have focused solely on permeation and have not reported amounts of the active remaining on and in the skin at the end of the IVPT. Therefore, a particular focus of this article is determination of the complete mass balance of the drug. It is noteworthy that for the evaluation of bioequivalence of topical formulations the draft guideline issued by the European Medicines Agency (EMA) requires the IVPT method to report on both the skin deposition and distribution of the active in the skin as well as amount permeated. Other aspects of current guidance from the EMA and United States Food and Drug Agency for IVPT are also compared and contrasted. Ultimately, harmonisation of IVPT protocols across the regulatory agencies will expedite the development process for novel topical formulations as well as the availability of generic products.

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Hepatic clearance (CLH ) prediction is a critical parameter to estimate human dose. However, CLH underpredictions are common, especially for slowly metabolized drugs, and may be attributable to drug properties that pose challenges for conventional in vitro absorption, distribution, metabolism, and elimination (ADME) assays, resulting in nonvalid data, which prevents in vitro to in vivo extrapolation and CLH predictions. Other processes, including hepatocyte and biliary distribution via transporters, can also play significant roles in CLH Recent advances in understanding the interplay of metabolism and drug transport for clearance processes have aided in developing the extended clearance model. In this study, we demonstrate proof of concept of a novel two-step assay enabling the measurement of multiple kinetic parameters from a single experiment in plated human primary hepatocytes with and without transporter and cytochrome P450 inhibitors-the hepatocyte uptake and loss assay (HUpLA). HUpLA accurately predicted the CLH of eight of the nine drugs (within twofold of the observed CLH ). Distribution clearances were within threefold of observed literature values in standard uptake and efflux assays. In comparison, the conventional suspension hepatocyte stability assay poorly predicted the CLH The CLH of only two drugs was predicted within twofold of the observed CLH Therefore, HUpLA is advantageous by enabling the measurement of enzymatic and transport processes concurrently within the same system, alleviating the need for applying scaling factors independently. The use of primary human hepatocytes enables physiologically relevant exploration of transporter-enzyme interplay. Most importantly, HUpLA shows promise as a sensitive measure for low-turnover drugs. Further evaluation across different drug characteristics is needed to demonstrate method robustness. SIGNIFICANCE STATEMENT: The hepatocyte uptake and loss assay involves measuring four commonly derived in vitro hepatic clearance endpoints. Since endpoints are generated within a single test system, it blunts experimental error originating from assays otherwise conducted independently. A key advantage is the concept of removing drug-containing media following intracellular drug loading, enabling the measurement of drug reappearance rate in media as well as the measurement of loss of total drug in the test system unencumbered by background quantities of drug in media otherwise present in a conventional assay.

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The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN. We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance.

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