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- Herausgeber: Bentham Science Publishers
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- Sprache: Englisch
AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology offers a comprehensive exploration of how artificial intelligence (AI) is revolutionizing the pharmaceutical and healthcare sectors. This book addresses the AI’s role in drug discovery, development, and delivery, highlighting applications in personalized medicine, nanotechnology, and clinical trials. It also covers AI’s impact on community and hospital pharmacy, herbal medicine, and drug product design.
Each chapter examines the use of AI in optimizing drug processes, from designing innovative therapies to improving regulatory compliance and future trends in pharmaceutical technology. This insightful resource is invaluable for researchers, pharmaceutical professionals, and healthcare innovators aiming to advance therapeutic outcomes through AI.
Key Features:
- Comprehensive coverage of AI applications in drug discovery, delivery, and design.
- Insights into AI-driven personalized medicine and nanotechnology.
- Regulatory perspectives on AI in drug delivery and medical devices.
- Future trends and innovations in AI for pharmaceutical technology.
Readership:
Ideal for pharmaceutical scientists, AI researchers, and healthcare professionals.
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Seitenzahl: 469
Veröffentlichungsjahr: 2024
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FOREWORD
In the vast landscape of pharmaceutical sciences, the confluence of artificial intelligence and drug delivery has emerged as a revolutionary force, redefining the boundaries of therapeutic advancements. "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology" stands as a beacon, guiding us through the transformative journey of technology-driven progress in healthcare.
This edited volume brings together a collection of insightful perspectives and groundbreaking research, offering a comprehensive exploration of the symbiotic relationship between artificial intelligence and pharmaceutical sciences. As we navigate the complexities of drug development, personalized medicine, and intelligent drug delivery systems, this book serves as a compass, pointing toward the future of healthcare innovation.
The esteemed editors have curated a diverse array of contributions from leading experts, scholars, and practitioners in the field. Each chapter unfolds a unique facet of the dynamic interplay between AI and pharmaceuticals, providing readers with a nuanced understanding of the challenges, opportunities, and ethical considerations that accompany this technological revolution.
The pace of change in the pharmaceutical landscape demands continuous reflection and collaboration. This volume not only reflects the current state of AI innovations but also serves as a catalyst for future explorations and breakthroughs. It is a testament to the collective commitment to advancing therapy through the seamless integration of technology.
As we embark on this intellectual journey, I commend the editors, contributors, and all those involved in bringing this book to fruition. May "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology" inspire and inform, contributing significantly to the ongoing dialogue in this transformative field.
PREFACE
Welcome to the forefront of transformative innovation in the intricate realms of drug delivery and pharmaceutical sciences. In this edited volume, "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology," we embark on an enlightening exploration into the dynamic intersection of Artificial Intelligence and healthcare. As editors, our goal is to present a compendium of cutting-edge research and insights that underscore the revolutionary impact of AI on the evolution of therapeutic interventions.
The landscape of drug delivery and pharmaceuticals is undergoing a paradigm shift, driven by the fusion of artificial intelligence and technology. This book serves as a collective effort, bringing together diverse perspectives, expertise, and research contributions from leading scholars and practitioners in the field. Each chapter is a testament to the collaborative endeavor to unravel the potential of AI in enhancing drug delivery systems, optimizing treatment regimens, and ultimately advancing the efficacy of therapeutic approaches.
The contributors to this volume are pioneers and thought leaders, each contributing a unique facet to our collective understanding of the synergies between AI and pharmaceutical sciences. From intelligent drug design and personalized medicine to the challenges and ethical considerations in AI applications, this book provides a comprehensive tapestry of the multifaceted impact of technology on the pharmaceutical landscape.
As editors, we are delighted to present this curated collection that reflects the current state of the art in AI innovations in drug delivery and pharmaceutical sciences. We believe that this book will serve as a valuable resource for researchers, students, practitioners, and anyone intrigued by the unfolding narrative of technology-driven advancements in healthcare.
We extend our gratitude to all the contributors for their scholarly endeavors and dedication to pushing the boundaries of knowledge. It is our sincere hope that this book stimulates further exploration, sparks insightful discussions, and inspires future breakthroughs in the ever-evolving field of AI and pharmaceutical sciences.
Enjoy the journey through the pages of "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology".
ACKNOWLEDGEMENT
The compilation of "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology" has been a collaborative endeavor shaped by the dedication, expertise, and support of numerous individuals and organizations. As editors, we extend our heartfelt appreciation to all those who have played a significant role in bringing this volume to fruition.
First and foremost, we express our gratitude to the contributing authors whose insightful research and scholarly contributions have enriched the content of this book. Your commitment to advancing the frontiers of knowledge in AI and pharmaceutical sciences has been instrumental in creating a comprehensive and valuable resource.
We extend our sincere thanks to the reviewers who lent their expertise and time to ensure the quality and rigor of the chapters included in this volume. Your constructive feedback has been invaluable in refining the content and maintaining the high standards of this work.
We are grateful to the publishers and editorial team who have worked tirelessly to bring this project to fruition. Your professionalism, guidance, and commitment to excellence have been crucial in shaping the final product.
Our appreciation extends to the academic institutions and research organizations that have provided a conducive environment for the contributors to pursue their innovative research in the field of AI and pharmaceutical sciences.
Last but not least, we thank our families, friends, and colleagues for their unwavering support, encouragement, and understanding throughout the editorial process. Your belief in our vision and commitment to this project have been a source of inspiration.
This book is a collective achievement, and we acknowledge the collaborative efforts of everyone involved. It is our hope that "AI Innovations in Drug Delivery and Pharmaceutical Sciences: Advancing Therapy through Technology" contributes meaningfully to the ongoing discourse in this dynamic field.
Thank you.
List of Contributors
An Overview of Artificial Intelligence (AI) In Drug Delivery and Development
Rakesh E. Mutha1,Vishal S. Bagul1,Rahul S. Tade1,*,Kuldeep Vinchurkar2Abstract
The integration of Artificial Intelligence (AI) into pharmaceutical research represents a transformative leap in drug development, addressing the challenges posed by complex diseases and traditional methodologies. In this comprehensive overview, we explore the historical evolution of AI's role in pharmaceutical research and its crucial importance in drug delivery and development. The foundational elements of AI in drug delivery and development are elucidated through an in-depth analysis of machine learning (ML) algorithms, deep learning techniques, and natural language processing in bioinformatics. These form the bedrock for understanding the subsequent chapters that unravel the emerging roles of AI in drug discovery, formulation, and delivery. An insightful examination of drug repurposing and interaction reveals AI-driven strategies, providing new therapeutic avenues. The chapters further unravel AI's impact on pharmacokinetics, pharmacodynamics, and its data-driven approaches for dose optimization. Clinical trials and patient recruitment witness a revolution through AI, optimizing design and ensuring regulatory compliance and safety. This chapter promises a holistic understanding of the symbiotic relationship between AI and pharmaceuticals, offering a roadmap for innovation and efficiency in the pursuit of advanced healthcare solutions.
INTRODUCTION
The integration of artificial intelligence (AI) into pharmaceutical research has ushered in a new era of innovation and efficiency. This burgeoning field repre-
sents a convergence of advanced computational techniques and the intricacies of drug development. As the complexity of disease continues to challenge traditional research methodologies, AI emerges as a transformative force, offering novel solutions and insights. The amalgamation of AI and pharmaceuticals not only expedites the drug discovery process but also enhances precision and efficacy in treatment strategies [1].
The primary motivation behind the incorporation of AI into pharmaceutical research lies in its ability to analyze vast datasets with unprecedented speed and accuracy. Traditional drug discovery methods often face bottlenecks in data processing, limiting the scope and pace of research. AI, equipped with ML algorithms, can discern patterns and correlations within data, accelerating the identification of potential drug candidates and streamlining the initial stages of drug development. This synthesis of computational power and pharmaceutical expertise marks a paradigm shift, allowing researchers to explore a broader landscape of possibilities [2].
Historical Perspective and Evolution Role of AI in Pharmaceutical Research
The historical trajectory of AI in pharmaceutical research is a fascinating narrative of evolution and adaptation. In its nascent stages, AI was primarily employed for basic tasks such as data organization and analysis. However, as computational capabilities advanced, AI found its niche in drug discovery. Early applications focused on the virtual screening of chemical compounds, predicting potential drug candidates with a level of efficiency previously unattainable. Over time, the role of AI in pharmaceutical research has expanded, encompassing molecular modelling, target identification, and even clinical trial optimization [3] (Table 1).
The evolution of AI in pharmaceuticals is not solely technological; it is also a testament to the collaborative efforts between computer scientists, biologists, chemists, and clinicians. Interdisciplinary collaboration has been pivotal in refining AI algorithms to suit the intricate demands of pharmaceutical research. Today, AI-driven platforms not only assist in the identification of potential drug targets but also contribute significantly to the optimization of experimental design, minimizing resource utilization and expediting the transition from bench to bedside [16].
Importance of AI in Drug Delivery and Development
Traditional drug development often employs a one-size-fits-all model, but AI enables the customization of treatments based on genetic, environmental, and lifestyle factors, enhancing therapeutic outcomes. AI has emerged as a transformative force in various industries, and its impact on drug delivery and development within the pharmaceutical sector is particularly noteworthy. The integration of AI technologies into these processes has the potential to revolutionize the way drugs are discovered, developed, and delivered, leading to more efficient and effective healthcare solutions [17].
In drug development, AI plays a crucial role in optimizing clinical trials. By analyzing patient data, AI algorithms can identify suitable candidates for trials, improving patient selection and increasing the likelihood of successful outcomes. AI can also enhance the efficiency of clinical trial design, helping researchers identify optimal dosages and treatment regimens. This not only accelerates the development timeline but also reduces the costs associated with failed trials [18].
Furthermore, AI contributes to personalized medicine by analyzing individual patient data to tailor drug treatments based on genetic, lifestyle, and environmental factors. This allows for more precise and targeted therapies, minimizing side effects and improving overall treatment efficacy. Personalized medicine holds great promise for the future of healthcare, and AI is a key enabler in making it a reality [19]. Additionally, AI supports the optimization of supply chain management in the pharmaceutical industry. Predictive analytics powered by AI can forecast demand, streamline production, and ensure the timely delivery of drugs to the market, improving overall efficiency and reducing costs [20]. A schematic of a typical drug development process for a pharmacologically active drug molecules is given in Fig. (1).
FOUNDATIONS OF AI IN DRUG DELIVERY AND DEVELOPMENT
The foundations of AI in drug delivery and development lie in its ability to process and analyze vast amounts of data, leading to more efficient and targeted approaches to discovering, formulating, and delivering drugs. The synergy between AI and pharmaceutical sciences holds great promise for advancing healthcare and improving patient outcomes.
Machine Learning Algorithms in Drug Discovery
ML algorithms, a subset of AI, have become indispensable tools in the early stages of drug development. These algorithms analyze vast datasets, identifying patterns and relationships that may elude human researchers. One prominent application of ML in drug discovery is the prediction of molecular activities. ML algorithms can sift through extensive chemical databases, recognizing structures that exhibit potential therapeutic effects. This accelerates the screening process, reducing the time and resources traditionally required for identifying viable drug candidates [22].
Fig. (1)) A typical drug development process for pharmaceutically active molecules [21].Additionally, ML aids in target identification, a crucial step in drug development. By analyzing biological data, including genetic information and protein interactions, algorithms can pinpoint specific molecules or pathways associated with diseases. This targeted approach streamlines the drug discovery process, enabling researchers to focus on candidates with higher probabilities of success. The integration of ML in drug discovery not only expedites the identification of potential drugs but also optimizes the entire research pipeline. As technology advances, the synergy between ML and pharmaceutical research continues to evolve, promising more effective and personalized therapeutic interventions [23, 24].
Deep Learning Techniques in Pharmaceutical Research
Deep learning, a subset of ML, has garnered considerable attention in pharmaceutical research due to its ability to handle complex and high-dimensional data. In drug discovery, where datasets are often intricate and multifaceted, deep learning techniques offer unique advantages. Traditional methods struggle to capture intricate relationships within biological systems, but deep learning models, such as neural networks, excel in discerning complex patterns. This enables more accurate predictions of how potential drugs may interact with biological targets, paving the way for the development of highly specific and effective therapeutics [25].
The power of deep learning extends to virtual screening, a critical step in drug development. Deep neural networks can analyze molecular structures and predict their binding affinities with target proteins. This virtual screening expedites the identification of promising drug candidates, reducing the need for extensive laboratory testing [26].
Natural Language Processing (NLP) in Bioinformatics
Natural Language Processing (NLP), a branch of AI focused on the interaction between computers and human language, has found a valuable niche in bioinformatics, particularly in the analysis of the vast biomedical literature and clinical records [27]. NLP algorithms, however, can automatically extract and organize key information such as gene-disease associations, drug interactions, and clinical outcomes. This not only accelerates the literature review process but also facilitates the identification of potential drug targets and biomarkers. NLP also plays a crucial role in clinical decision-support systems. By analyzing electronic health records and patient narratives, NLP algorithms can assist healthcare professionals in extracting valuable insights. This includes identifying patterns in patient responses to specific drugs, detecting adverse reactions, and providing real-time information to aid in treatment decisions [28, 29].
EMERGING ROLE OF AI IN DRUG DELIVERY AND DEVELOPMENT
The field of drug delivery and development is undergoing a transformative phase with the integration of AI. As the complexity of disease increases and the traditional drug discovery process faces challenges, AI emerges as a powerful tool to streamline and enhance various stages of drug development. This article explores the pivotal role of AI in revolutionizing drug delivery and development, focusing on the drug discovery process, target identification and validation, and high-throughput screening and compound design.
The Drug Discovery Process and its Challenges
The drug discovery process is a complex and time-consuming journey that involves multiple stages, including target identification, lead compound identification, preclinical and clinical trials, and regulatory approval. Despite significant advancements in science and technology, the success rate in bringing new drugs to the market remains low, and the process is plagued by high costs and long timelines [30].
One of the major challenges in drug discovery is the identification of suitable drug targets. Traditional methods heavily rely on trial and error, making the process time-consuming and resource-intensive. Additionally, the gap between preclinical and clinical success rates poses a significant challenge. AI addresses these challenges by providing data-driven insights, predictive analytics, and efficient decision-making tools [31] (Fig. 2).
Fig. (2)) AI-based drug discovery processes.Use of AI in Target Identification and Validation
AI plays a crucial role in target identification and validation, significantly expediting the early stages of drug discovery. By leveraging large datasets, AI algorithms can analyze biological information, identify potential drug targets, and validate their relevance in the context of specific diseases. ML algorithms can process vast amounts of biological and clinical data to uncover patterns and associations that may not be apparent through traditional methods. This enables researchers to prioritize targets with a higher likelihood of success, reducing the risk of investing resources in unsuccessful avenues. Furthermore, AI facilitates the identification of biomarkers that can aid in patient stratification, allowing for more personalized and targeted therapeutic approaches [22, 32].
The integration of AI in target identification and validation enhances the efficiency of the drug discovery pipeline, leading to faster and more cost-effective development of novel therapeutics.
High-throughput screening and Compound Design AI
High-throughput screening (HTS) is a crucial step in drug discovery, involving the rapid testing of thousands of compounds to identify potential drug candidates. AI accelerates this process by optimizing the experimental design, analyzing screening data, and predicting the biological activity of compounds. ML models can learn from large datasets of chemical and biological information, enabling the identification of compounds with desired pharmacological properties. This not only expedites the identification of lead compounds but also facilitates the design of novel molecules with improved drug-like properties [33, 34].
As technology continues to advance, the collaboration between AI and traditional drug development approaches will likely become even more integral, paving the way for a new era of precision medicine and personalized therapeutics. The emerging role of AI in drug delivery and development holds the promise of accelerating the pace at which novel and effective treatments reach patients in need.
CASE STUDIES AND EXAMPLES OF SUCCESSFUL DRUG DISCOVERY AI
In recent years, the integration of AI in drug discovery has revolutionized the pharmaceutical industry, accelerating the identification and development of novel therapeutic compounds. Several case studies illustrate the successful application of AI in drug discovery, demonstrating its potential to streamline the process and enhance efficiency.
Ebola Inhibitor Discovery by Atomwise
Atomwise, a company specializing in AI for drug discovery, utilized deep learning to identify potential inhibitors for the Ebola virus. In 2016, they collaborated with researchers at the University of Toronto to screen existing drugs for their ability to inhibit Ebola infection. Atomwise's AI platform analyzed the 3D structures of chemical compounds and predicted their potential efficacy against the virus. The result was the discovery of two promising compounds that demonstrated inhibitory effects on Ebola, showcasing the efficiency of AI in repurposing existing drugs for new therapeutic purposes [35, 36].
Benevolent AI Treatment for Amyotrophic Lateral Sclerosis (ALS)
Benevolent AI employed AI algorithms to identify a potential treatment for Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease. By analyzing vast datasets, including scientific literature, clinical trial data, and biological databases, the AI system identified a previously overlooked molecule with the potential to modulate the underlying mechanisms of ALS. Subsequent preclinical experiments validated the efficacy of the identified molecule, providing a promising avenue for the development of a novel ALS treatment [37, 38].
AlphaFold and Protein Folding by DeepMind
DeepMind's AlphaFold, an advanced AI system for predicting protein structures, made significant strides in solving one of biology's grand challenges. In the Critical Assessment of Structure Prediction (CASP) competition, AlphaFold demonstrated unparalleled accuracy in predicting the 3D structures of proteins. This breakthrough has profound implications for drug discovery, as understanding protein structures is crucial for designing targeted therapeutics. AlphaFold's success exemplifies the potential of AI to revolutionize our understanding of molecular biology and accelerate drug development [39, 40].
Drug Repurposing Model by Recursion Pharmaceuticals
Recursion Pharmaceuticals leveraged AI to identify new therapeutic uses for existing drugs. Their platform combines AI, automation, and experimental biology to rapidly assess the impact of thousands of compounds on various diseases. By systematically repositioning existing drugs for new indications, Recursion has identified potential treatments for rare genetic diseases. This approach not only expedites the drug discovery process but also maximizes the utility of known compounds, demonstrating the versatility of AI in uncovering novel therapeutic applications [41].
IBM Watson for Drug Discovery
IBM Watson, known for its cognitive computing capabilities, has been applied to drug discovery through its Watson for Drug Discovery platform. This system integrates and analyzes vast amounts of biomedical literature, clinical trial data, and genomic information to identify potential drug candidates and biomarkers. The platform has been employed in collaboration with pharmaceutical companies to expedite the discovery of new drugs for cancer and other diseases. IBM Watson's approach showcases the power of AI in synthesizing diverse data sources to generate actionable insights in drug development [42].
AI-Driven Drug Discovery by Exscientia
Exscientia, a company specializing in AI-driven drug discovery, entered into a partnership with Sumitomo Dainippon Pharma to expedite the development of new drugs. Through the collaboration, Exscientia's AI platform identified a novel compound for the treatment of obsessive-compulsive disorder (OCD). The AI system efficiently explored a vast chemical space, optimizing the compound's properties for therapeutic efficacy. This collaboration exemplifies the potential for AI to enhance traditional pharmaceutical research and development processes through strategic partnerships [43].
DRUG FORMULATION AND DELIVERY
Advancements in technology, particularly in the realm of AI, have revolutionized various industries, and drug formulation and delivery are no exceptions. The integration of AI in pharmaceutical research and development has significantly enhanced the efficiency and precision of drug design, formulation, and delivery systems. In this section, we will explore key aspects of AI applications in drug formulation and optimization, personalized medicine, and controlled drug delivery systems.
AI Applications in Drug Formulation and Optimization
AI has become an indispensable tool in drug formulation and optimization, streamlining the drug development process and reducing the time and resources required. One notable application is the use of ML algorithms to analyze vast datasets and predict optimal drug formulations. Software platforms like Atomwise and IBM Watson for Drug Discovery utilize AI to sift through chemical databases and predict potential drug candidates.
Atomwise, for example, employs deep learning models to analyze the interaction between drugs and their target proteins. This accelerates the identification of potential compounds that may be effective in treating specific diseases. IBM Watson for Drug Discovery utilizes AI to analyze scientific literature, clinical trial data, and other relevant sources to identify potential drug candidates and optimize existing formulations [44, 45].
Personalized Medicine and AI in Dosage Customization
Personalized medicine, tailoring medical treatment to the individual characteristics of each patient, has gained prominence with the integration of AI. In drug delivery, AI plays a crucial role in dosage customization, ensuring that patients receive the right amount of medication based on their unique characteristics and responses.
AI algorithms analyze patient-specific data, such as genetic information, biomarkers, and health records, to predict how individuals will respond to different drug dosages. This allows healthcare professionals to optimize treatment plans for maximum efficacy and minimal side effects. For instance, Tempus, a technology company, employs AI to analyze clinical and molecular data to help oncologists personalize cancer treatment based on the patient's genetic profile. Dosage customization through AI not only improves treatment outcomes but also minimizes adverse reactions, ultimately enhancing the overall quality of patient care [46].
Controlled Drug Delivery Systems and AI-Driven Innovations
Controlled drug delivery systems aim to release drugs at a predetermined rate or target specific sites within the body, optimizing therapeutic effects while minimizing side effects. AI has played a pivotal role in the innovation of these systems. Smart drug delivery devices, equipped with sensors and AI algorithms, monitor real-time physiological parameters and adapt drug release accordingly. For example, insulin pumps for diabetes management use AI to analyze continuous glucose monitoring data and adjust insulin delivery to maintain optimal blood glucose levels.
Furthermore, AI-driven innovations in nanotechnology enable the development of nanocarriers for drug delivery. These nanocarriers, such as liposomes or nanoparticles, can be precisely engineered to release drugs in response to specific stimuli, such as pH changes or enzymatic activity. This level of control enhances the efficiency and specificity of drug delivery, reducing side effects and improving patient compliance [47].
DRUG REPURPOSING AND DRUG-DRUG INTERACTIONS
In the dynamic realm of healthcare, the tandem exploration of drug repurposing and Drug-Drug Interactions (DDIs) stands as a beacon of innovation. Drug repurposing, the re-evaluation of existing medications for novel therapeutic purposes, offers a streamlined path to discovery. Simultaneously, understanding the intricate web of interactions between drugs becomes pivotal for optimizing treatment outcomes.
Repurposing a drug may encompass changes such as adjusting dosage, altering formulation, introducing a new method of use, or targeting a different patient population. Other terms used to describe this process include re-profiling of the drug, re-tasking of the drug, rescue of the drug, etc. [48]. A pressing requirement exists for the establishment of a study framework to address the disease using safe and efficacious therapeutic alternatives. In contrast to the process of discovery of a new drug, repurposing of the drug presents a potentially more economical with an expedited paradigm for exploring treatment options by leveraging existing drugs [49].
Structure-based Drug Repurposing
The objective of the same is to order potential repurposing candidates based on affinity to disease-causing proteins [50]. Achieving a high-resolution viral structure involves sophisticated crystallization technology, cryo-electron microscopy, and tomography [51], processes known for their lengthy and expensive nature. AI introduces a novel and fast approach for forecasting the structure of crucial proteins essential for viral entry and duplication. Additionally, AI holds the potential to aid in complex structure identification using images of cryo-electron microscopes. The protein structure of SARS-CoV-2 has been extracted from a high-resolution density map obtained from a cryo-electron microscope using a specialized deep convolutional neural network called DeepTracer [52].
While molecular docking continues to be a widely used technique for virtual screening ligands to uncover potential therapeutic applications, its effectiveness is constrained due to the significant computational expense and the expansive chemical space. ML, particularly deep learning, has emerged as a promising tool to enhance drug repurposing based on molecular docking. This is achieved by creating models that correlate docking scores with chemical information [53, 54].
Genomics-based Drug Repurposing
Genomic-based drug repurposing operates on the assumption that (1) a negative correlation between a drug's transcriptomic and disease signature suggests the potential for the drug in the treatment of the disease, and (2) the indications for both drugs may be inter-changeable if there is a positive correlation seen in the transcriptome response profiles of the drugs [55].
AI offers a different way to computationally infer biological profiles, which in two cases makes genomics-based medication repurposing possible: (1) inferring transcriptomic profiles under specific conditions of experiment like dose, duration, and cell culture based on chemical data, and (2) designing new drugs from scratch based on drug transcriptomic profiles. Graph convolutional networks (GCN) and multilayered feed-forward neural networks are integrated into the DeepCE approach to forecasting the different expressions of gene profiles affected by new compounds in the LINCS project [56]. This approach was applied to COVID-19 drug repurposing.
Network Pharmacology-based Drug Repurposing
The above stated method is one of the highly established methods that integrate systems of bioinformatics and biology to untangle intricate correlations of drugs, targets, and diseases [57]. In this approach, the interactions of various biological nodes can be established through investigational findings or statistical understanding.
Exploring certain opportunities could significantly enhance AI-driven network pharmacology-based drug repurposing. For instance, the construction of protein-protein interaction (PPI) networks could be refined by incorporating various data sources such as gene fusion, co-expression, and co-existence. Diverse correlation models can be used in network modeling, employing techniques like Graph Neural Networks (GNNs), potentially offering improved resolution for uncovering hidden relationships in the context of COVID-19 drug repurposing. AI plays a crucial role in integrating diverse and heterogeneous biological networks for more effective drug repurposing. The introduction of a novel AI-based drug repurposing technique called deep-dotnet, is leveraging diversified biological network information across diverse biotic individuals to forecast new interactions of drugs with targets more accurately than existing techniques [58].
Mechanism-driven Drug Repurposing
Drug repurposing driven by mechanisms aims to identify drugs based on basic mechanisms or hypotheses related to the disease. For instance, clinical side effects, representing patient phenotypic responses to specific drugs, can serve as valuable indicators to discover new therapeutic uses [59]. The Phenome-Wide Association Study (PheWAS) serves as a crucial mechanism-driven strategy for drug repurposing. It intends to investigate new genetic variants and disease associations by analyzing clinical data within a vast array of electronic medical records (EMRs). Through this approach, possible medication alternatives for an ailment can be detected by assessing whether a drug has the potential to influence genetic variants associated with the disease [60].
The gathered understanding of the natural progression of SARS-CoV-2 is an asset in the development of treatments. Biomedical literature serves as a key foundation, offering a wealth of information on COVID-19 and potential repurposing opportunities. Benevolent AI has created an abroad information charts abroad, incorporating a vast source of well-organized health data and its interrelationships, utilizing Monte Carlo tree search and symbolic AI approaches [61].
While AI-assisted drug repurposing can generate a prioritized list of drugs, confirming their efficacy and safety in a clinical setting poses a significant challenge due to the scarcity of reliable data [62]. Typically, candidates identified through computational methods are validated using in vitro/in vivo studies, current clinical testing, and literature surveys. Few drug candidates are immediately accepted for confirmation through newly designed clinical trials, highlighting the absence of identical determinants to assess the AI-driven drug repurposing performance. Despite the significant strides made by AI in advancing biomedical fields, there are still existing gaps in harnessing the accomplishment of AI within the regulatory framework. Various agencies of the government are actively encouraging the development of robust, safe, secure, and privacy-preserving ML. This initiative aims to order AI’s translational and fundamental research in alignment with the precedence set by the administration [49].
Predicting Drug Interactions and Adverse Effects
Understanding and predicting drug-drug interactions (DDIs) is a crucial element in drug research, with the potential to cause adverse effects on patients and result in serious consequences. Accurate prediction of these interactions is vital for enhancing the clinician’s decision-making and establishing optimal treatment plans. Nevertheless, the manual detection of these interactions is a laborious and lengthy task. Leveraging the advancements in AI is imperative to achieve precise forecasts of DDIs [63].
In recent years, significant advancements in drug databases have led to the refinement of various computer-based techniques for predicting drug-drug interactions (DDIs). These methods encompass both ML and deep learning approaches, capitalizing on extensive big data resources. In 2013, Vilar et al. introduced a technique using interaction profile fingerprints to assess drug pairs' similarity and extrapolated new DDIs for combinations with non-objectionable interaction [64]. While the said approach heavily relied on proceeding known DDIs and overlooked additional drug data. Subsequently, a similarity-based approach was adopted, assuming that similar drugs may interact similarly. Vilar et al. made an application of this technique for the prediction of new DDIs based on similarity in structure and interaction profile fingerprints [65]. Following a similar trajectory, Cheng et al. developed a support vector machine model incorporating attributes obtained through a simplified molecular input line entry system (SMILES) and similarity information of side effects. Additionally, diverse feature extraction methods have been explored to enhance predictive performance.
PHARMACOKINETICSS AND PHARMACODYNAMICS
In the realm of pharmaceutical innovation, the integration of AI with pharmacokinetics and pharmacodynamics heralds a ground breaking era. Pharmacokinetics unravels drug absorption and metabolism, while pharmacodynamics explores their effects. Together with AI, this dynamic duo holds the potential to revolutionize drug development, refine dosage, and usher in an era of personalized medicine.
AI in Modeling Drug Pharmacokinetics
The optimization of pharmacokinetics (PK) plays a crucial role in drug discovery and development. Traditional approaches involve the use of animals in vivo pharmacokinetic information along with in vitro study details from both human and animal sources to assess PK in humans. However, in recent years, AI has come up as a significant instrument for modeling in vivo and human pharmacokinetics. These technologies enable early prediction of PK from chemical structures during the drug discovery phase. This advancement offers valuable prospects to guide the design and prioritize drugs based on relevant in vivo details, eventually allowing for the prediction of human PK at the early stages of drug design.
Enhancements in AI-based prediction of compound pharmacokinetics (PK) can be attained by improving the accuracy of models. This improvement is driven by enhancing the quality and size of datasets, particularly for human data. Additionally, incorporating in vitro/in vivo information or their ML estimates as showcased in the models can aid in predicting in vivo and human pharmacokinetics, respectively [66]. Transfer learning is a valuable methodology to address the challenge of small human pharmacokinetic information by understanding from in vivo information.
Approaches used for AI, including chemical structure representation and deep neural networks based on convolution of graphs offer higher accuracy, they are challenging to understand and provide restricted insights into the subsequent models. Explainable AI (XAI) techniques play a crucial role in providing mechanistic interpretations of model findings, offering data on SAR beneficial for drug optimization and decision-making. Applying and analyzing several XAI techniques to lead optimization datasets, Harren et al. [67] highlighted the potential of SHAP-based techniques. In comparison to small compounds, the application of AI in the prediction of biologics' pharmacokinetic details is less advanced. Developing AI-assisted pharmacokinetic models for antibodies is workable but necessitates the generation of sufficiently large datasets.
Predicting Drug Efficacy and Safety Profiles
Through the utilization of AI processes and ML practices, the complete drug innovation procedure has the potential for a profound transformation, presenting numerous advantages. A primary benefit lies in AI's ability to screen expansive compound libraries swiftly and efficiently, greatly improving the recognition of possible drug molecules. Additionally, the algorithms of AI could play a crucial role in the prediction of the safety and efficacy status of these drugs, providing valuable understandings and diminishing the trust in exhaustive preclinical and clinical studies. AI’s projected capabilities hold the promise of streamlining the process of drug development, increasing the possibility of clinical trial success, and eventually leading to the advent of extra effective and safer drugs [68].
Utilizing AI algorithms, it becomes possible to analyze the physicochemical properties of drugs, including lipophilicity, molecular weight, and ionization, to forecast the rates of drug clearance. Through dataset training containing details of the pathways of drug clearance, AI assists in elucidating the speed of elimination of drugs. Such insights are essential for establishing suitable dosing regimens and guaranteeing both the efficacy and safety of the drug [69].
Data-Driven Approaches for Dose Optimization
AI technologies can analyze and forecast PK profiles following drug administration. Additionally, they enable the examination of the correlation between drug exposure and response, accounting for various confounding factors. Such models prove valuable in refining the selection of dose and dosing regimen for a study [70, 71]. Notably, these models have the potential to assist in optimizing doses for specific populations with limited data, such as in rare disease studies, and pediatric and pregnant populations.
Although not as frequently employed in pharmacological research, reinforcement learning (RL) holds promise for applications in personalized medicine and optimized dosage. RL agents acquire optimal decision-making strategies through interaction with the environment and feedback in the form of rewards or penalties. Researchers have investigated the use of RL in determining drug dosages and developing adaptive treatment strategies [22].
Medications play a crucial role in maintaining human health, and the ongoing challenge for clinicians is to select the appropriate treatment and dosage for individual patients. Despite adherence to prescribed guidelines, drugs exhibit varied response rates and adverse impact profiles, posing a constant dilemma. This becomes particularly critical for medications used in treating severe conditions or with a narrow range between efficacy and toxicity. Initial dosages, guided by standard protocols, may not be optimal or safe for every patient, especially when drugs haven't been extensively evaluated across various doses and patient profiles.
