Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

A comprehensive primer and review of PROTACs and their In Silico design

The cutting-edge technique of Proteolysis Targeting Chimeras, or PROTACs, has gained significant attention as a viable approach for specific protein degradation. This innovative technology has vast potential in fields such as cancer therapy and drug development. The development of effective and specific therapies for a range of diseases is within reach with PROTACs, which can target previously "undruggable" proteins while circumventing the off-target effects of conventional small molecule inhibitors. This manuscript aims to discuss the application of in silico techniques to the design of these groundbreaking molecules and develop PROTAC complexes, in order to identify potential PROTAC candidates with favorable drug-like properties. Additionally, this manuscript reviews the strengths and weaknesses of these methods to demonstrate their utility and highlights the challenges and future prospects of in silico PROTAC design. The present review provides a valuable and beginner-friendly resource for researchers and drug developers interested in using in silico methods for PROTAC design, specifically ternary structure prediction.

Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like 'artificial intelligence', 'drug discovery', 'pharmacy research', 'clinical trial', etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services.

Computational tools for the prediction of site- and regioselectivity of organic reactions

The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational models for regio- and site-selectivity prediction, and the introduction of machine learning to the chemical sciences within the past decade has added a whole new dimension to these endeavors. This review article walks through the currently available predictive tools for regio- and site-selectivity with a particular focus on machine learning models while being organized along the individual reaction classes of organic chemistry. Respective featurization techniques and model architectures are described and compared to each other; applications of the tools to critical real-world examples are highlighted. This paper aims to serve as an overview of the field's status quo for both the intended users of the tools, that is synthetic chemists, as well as for developers to find potential new research avenues.

Artificial intelligence in the management of metabolic disorders: a comprehensive review

This review explores the significant role of artificial intelligence (AI) in managing metabolic disorders like diabetes, obesity, metabolic dysfunction-associated fatty liver disease (MAFLD), and thyroid dysfunction. AI applications in this context encompass early diagnosis, personalized treatment plans, risk assessment, prevention, and biomarker discovery for early and accurate disease management. This review also delves into techniques involving machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and reinforcement learning associated with AI and their application in metabolic disorders. The following study also enlightens the challenges and ethical considerations associated with AI implementation, such as data privacy, model interpretability, and bias mitigation. We have reviewed various AI-based tools utilized for the diagnosis and management of metabolic disorders, such as Idx, Guardian Connect system, and DreaMed for diabetes. Further, the paper emphasizes the potential of AI to revolutionize the management of metabolic disorders through collaborations among clinicians and AI experts, the integration of AI into clinical practice, and the necessity for long-term validation studies. The references provided in the paper cover a range of studies related to AI, ML, personalized medicine, metabolic disorders, and diagnostic tools in healthcare, including research on disease diagnostics, personalized therapy, chronic disease management, and the application of AI in diabetes care and nutrition.

Exploration of rutin derivatives as potential inhibitors of prostate cancer signaling pathways: A comprehensive in-silico study

Prostate cancer is a widespread health issue that affects men worldwide. It is one of the most common forms of cancer, and its development is influenced by a combination of hereditary, epigenetic, environmental, age, and lifestyle factors. Given that it is the second most common cause of cancer-related deaths in men, it is crucial to comprehend its complex facets. Present research especially targets the 3-kinase/protein kinase B, Epidermal Growth Factor Receptor, and extracellular signal-related kinase pathways, which are known to be significantly involved in prostate cancer progression. Here, Rutin derivatives were screened against selected prostate cancer targets. Molecular docking was performed to identify favorable interactions and the most promising compound. Further, Density functional theory, pharmacokinetics, Molecular dynamics simulation, principal component analysis, free energy landscape analysis, and Molecular Mechanics Poisson-Boltzmann Surface Area provided additional insights into selecting the best drug candidate. Among all the selected rutin derivatives, RU4b1 has potent inhibitory action. We also performed predictive analysis to identify the distinct metabolic sites within the structure of RU4b1. RU4b1 also exhibits drug-like properties and potent antioxidant activity. The findings were also compared with standard drugs and reference molecules of the respective proteins, and it is noteworthy that RU4b1 exhibited superior action compared to the standard drugs and reference molecules. This study aims to contribute valuable insights into developing targeted therapies for prostate cancer, emphasizing the potential of rutin derivatives as effective anti-cancer agents.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models

Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxic metabolites and drug-drug interactions. Therefore, it is of high importance to predict if a compound is the substrate of a given P450 in the early stage of drug development. In this study, we built the multitask learning models to simultaneously predict the substrates of five major drug-metabolizing P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the collected substrate data sets. Compared to the single-task model and conventional machine learning models, the multitask fingerprints and graph neural networks model achieved superior performance with the average AUC values of 90.8% on the test set. Notably, the multitask model demonstrated its good performance on the small amount of substrate data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley additive explanation and the attention mechanism were used to reveal specific substructures associated with P450 substrates, which were further confirmed and complemented by the substructure mining tool and the literature.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Arnicolide D: a multi-targeted anticancer sesquiterpene lactone-preclinical efficacy and mechanistic insights

Arnicolide D, a potent sesquiterpene lactone from Centipeda minima, has emerged as a promising anticancer candidate, demonstrating significant efficacy in inhibiting cancer cell proliferation, inducing apoptosis, and suppressing metastasis across various cancer models. This comprehensive study delves into the molecular underpinnings of Arnicolide D's anticancer actions, emphasizing its impact on key signaling pathways such as PI3K/AKT/mTOR and STAT3, and its role in modulating cell cycle and survival mechanisms. Quantitative data from preclinical studies reveal Arnicolide D's dose-dependent cytotoxicity against cancer cell lines, including nasopharyngeal carcinoma, triple-negative breast cancer, and human colon carcinoma, showcasing its broad-spectrum anticancer potential. Given its multifaceted mechanisms and preclinical efficacy, Arnicolide D warrants further investigation in clinical settings to validate its therapeutic utility against cancer. The evidence presented underscores the need for rigorous pharmacokinetic and toxicological studies to establish safe dosing parameters for future clinical trials.

Design strategies, advances and future perspectives of colon-targeted delivery systems for the treatment of inflammatory bowel disease

Inflammatory bowel diseases (IBD) significantly contribute to high mortality globally and negatively affect patients' qualifications of life. The gastrointestinal tract has unique anatomical characteristics and physiological environment limitations. Moreover, certain natural or synthetic anti-inflammatory drugs are associated with poor targeting, low drug accumulation at the lesion site, and other side effects, hindering them from exerting their therapeutic effects. Colon-targeted drug delivery systems represent attractive alternatives as novel carriers for IBD treatment. This review mainly discusses the treatment status of IBD, obstacles to drug delivery, design strategies of colon-targeted delivery systems, and perspectives on the existing complementary therapies. Moreover, based on recent reports, we summarized the therapeutic mechanism of colon-targeted drug delivery. Finally, we addressed the challenges and future directions to facilitate the exploitation of advanced nanomedicine for IBD therapy.

Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review

Cytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations.

Targeted Clindamycin Delivery Systems: Promising Options for Preventing and Treating Bacterial Infections Using Biomaterials

Targeted therapy represents a real opportunity to improve the health and lives of patients. Developments in this field are confirmed by the fact that the global market for drug carriers was worth nearly $40 million in 2022. For this reason, materials engineering and the development of new drug carrier compositions for targeted therapy has become a key area of research in pharmaceutical drug delivery in recent years. Ceramics, polymers, and metals, as well as composites, are of great interest, as when they are appropriately processed or combined with each other, it is possible to obtain biomaterials for hard tissues, soft tissues, and skin applications. After appropriate modification, these materials can release the drug directly at the site requiring a therapeutic effect. This brief literature review characterizes routes of drug delivery into the body and discusses biomaterials from different groups, options for their modification with clindamycin, an antibiotic used for infections caused by aerobic and anaerobic Gram-positive bacteria, and different methods for the final processing of carriers. Examples of coating materials for skin wound healing, acne therapy, and bone tissue fillers are given. Furthermore, the reasons why the use of antibiotic therapy is crucial for a smooth and successful recovery and the risks of bacterial infections are explained. It was demonstrated that there is no single proven delivery scheme, and that the drug can be successfully released from different carriers depending on the destination.

BioPrint meets the AI age: development of artificial intelligence-based ADMET models for the drug-discovery platform SAFIRE

Background: To prioritize compounds with a higher likelihood of success, artificial intelligence models can be used to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of molecules quickly and efficiently. Methods: Models were trained with BioPrint database proprietary data along with public datasets to predict various ADMET end points for the SAFIRE platform. Results: SAFIRE models performed at or above 75% accuracy and 0.4 Matthew's correlation coefficient with validation sets. Training with both proprietary and public data improved model performance and expanded the chemical space on which the models were trained. The platform features scoring functionality to guide user decision-making. Conclusion: High-quality datasets along with chemical space considerations yielded ADMET models performing favorably with utility in the drug discovery process.

DrugBank 6.0: the DrugBank Knowledgebase for 2024

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

In vitro evaluation of tropolone absorption, metabolism, and clearance

Tropolone compounds can inhibit hepatitis B virus (HBV) replication at sub-micromolar levels and are synergistic upon co-treatment with nucleos(t)ide analog drugs. However, only a few compounds within this chemotype have been screened for their pharmacological properties. Here, we chose 36 structurally diverse tropolones from six subclasses to characterize their in vitro pharmacological parameters. All compounds were more soluble in pHs that reflect the gastrointestinal tract (pH 5 and 6.5) than plasma (pH 7.4). Those compounds that had solubility limits >100 μM were tested in a passive permeability assay, and there was no general trend in the compounds' passive permeability at any pH. Twenty-nine compounds with the best absorption parameters were tested in HEK293 cells to assess potential cytotoxicity; measured toxicities were similar to those in the hepatic HepDES19 cells used for screening (R2 = 0.55). Sixteen representative compounds were tested against five major CYP450 isoforms and there was no substantial inhibition by any compound against any of the enzymes tested (<50%). The t1/2 values of 15 compounds were determined in the microsome stability assay and 12 compounds were evaluated in plasma protein binding assays to assess factors affecting their rate of clearance. All compounds with detectable analyte peaks had t1/2 > 30 min, and while 4 of 12 had statistically significant decreased potency in conditions with increased albumin concentrations, only one compound's potency was biologically significant. These data indicate that the tropolones have pharmacological characteristics that reflect approved drugs and inform future structure activity relationships during drug design.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

INTRODUCTION

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine

Trillions of diverse microbes reside in the gut and are deeply interwoven with the human physiological process, from food digestion, immune system maturation, and fighting invading pathogens, to drug metabolism. Microbial drug metabolism has a profound impact on drug absorption, bioavailability, stability, efficacy, and toxicity. However, our knowledge of specific gut microbial strains, and their genes that encode enzymes involved in the metabolism, is limited. The microbiome encodes over 3 million unique genes contributing to a huge enzymatic capacity, vastly expanding the traditional drug metabolic reactions that occur in the liver, manipulating their pharmacological effect, and, ultimately, leading to variation in drug response. For example, the microbial deactivation of anticancer drugs such as gemcitabine can lead to resistance to chemotherapeutics or the crucial role of microbes in modulating the efficacy of the anticancer drug, cyclophosphamide. On the other hand, recent findings show that many drugs can shape the composition, function, and gene expression of the gut microbial community, making it harder to predict the outcome of drug-microbiota interactions. In this review, we discuss the recent understanding of the multidirectional interaction between the host, oral medications, and gut microbiota, using traditional and machine-learning approaches. We analyze gaps, challenges, and future promises of personalized medicine that consider gut microbes as a crucial player in drug metabolism. This consideration will enable the development of personalized therapeutic regimes with an improved outcome, ultimately leading to precision medicine.