A Handbook of Artificial Intelligence in Drug Delivery

A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies. Machine Learning (ML) is a subset of the umbrella term Artificial Intelligence (AI). AI has already crept into several tasks of our day-to-day life, like digital assistants, internet surfing, online shopping, etc. Machine learning (ML), as the name indicates, is a way (algorithm) of self-learning by computer. The development of ML algorithms originated from the quest of computers that learn on their own based on their experiences. The learning takes place with the help of a dataset provided to the computer as training data. It basically helps in decision making or prediction of an outcome when the situation is having manifold factors and when decision making is not straightforward as per human intelligence. Drug discovery and delivery is a complicated process requiring a lot of human aptitudes and decision-making ability. The process is characterized by abundant data handling with multiple variables, thus making it amenable to the application of ML. Opportunities for the application of ML occur at nearly all stages of drug discovery, like target identification and validation, compound screening, lead identification and optimization, preclinical development, clinical trials, and biomarker identification and analysis. However, for the effective application of ML, its basic understanding is inevitable. The knowledge and technology about ML in healthcare are advancing considerably. Various software libraries are available online that can work with a range of hardware, even simple personal computers. Proper understanding and selection of an appropriate Machine Learning approach may provide accurate predictions. This chapter will provide various ML approaches and their areas of applications with suitable examples. Several ambiguities in the available methods of ML are cropping up as these are being applied to actual situations in the healthcare sector. Cover A Handbook of Artificial Intelligence in Drug Delivery Copyright Contributors An overview of artificial intelligence in drug development Introduction Impact of AI on drug development AI in drug repurposing AI in developing improved policies Conclusion References General considerations on artificial intelligence The introduction of AI and its importance in pharmaceutical operations Role of ML in drug design and drug delivery Artificial intelligence in drug design Databases for virtual screening Gold-standard datasets Applications of DNNs in VS Feed-forward DNNs Recurrent neural networks Application of AI in biomedical and tissue engineering Wearables in clinical trials AI in semantic annotation of healthcare data AI in tissue engineering Challenges in applications of ML to healthcare data Artificial intelligence integration with nanotechnology References Role of artificial intelligence in quality profiling and optimization of drug products The concept of quality in drug formulation development Quality concept and definitions Quality and quality control QbD concept and benefits QbD approach in biotechnology products Risk management, risk assessment, and optimization Risk management Risk assessment Analytical quality by design Role of AI and ML models on determining of quality profile and optimization studies AI in pharmaceutical manufacturing AI in advancing pharmaceutical product development AI in the lifecycle of pharmaceutical products Machine learning algorithms K nearest neighbor Support vector regression Classification and RT Bagging trees Random forest Gradient boosting machine Extreme gradient boosting Artificial neural network Comparison of Java and Python programming languages Key differences between Python and Java Conclusion References AI applications for multivariate control in drug manufacturing Artificial intelligence AI in pharmaceutical manufacturing Process validation and multivariate control Continued process verification and drug manufacturing control Artificial intelligence considered from a multivariate perspective Data and multivariate analysis First requirement for MVA in biopharma: Data quality Data governance Data transformation as a required step under data governance Use cases of AI application in drug manufacturing AI and regulations AI resources Conclusions References AI approaches for the development of drug delivery systems Introduction Artificial neural networks Applications of ANNs in drug delivery system design AI approach to predicting drug release profiles AI methods for optimization of drug delivery systems Microspheres and microparticles Implants and transdermal products Optimization of inhalers Nanomedicines Conclusions References Artificial neural network (ANN) in drug delivery Introduction Modeling the drug efficiency ANN in the prediction of drug properties Prediction of drug properties Prediction of toxicity Prediction of adverse drug reactions ANN in drug formulation Effectiveness of drug dosing Stability of active pharmaceutical ingredients ANN in drug administration Efficacy of the loading Membrane interaction and cellular uptake prediction Drug-target interactions ANN in targeted drug delivery Modeling of drug delivery using carriers Control of micro-/nanorobots Control of nanomaterials ANN in the prediction and monitoring of drug release profile ANN in personalized medicine delivery Prospective of AI application in future drug delivery systems Conclusion References Relevance of AI in microbased drug delivery system Introduction General considerations on the broad utility of AI for designing novel microsystems for drug delivery systems Types of formulation using ANN Microspheres Lipid-based carriers Lipid microparticles Liposome Microemulsion Liquid crystals Solid dispersion Prospects and challenges References Further reading Application of artificial intelligence driving nano-based drug delivery system Introduction Artificial intelligence description Nanoscience and nanotechnology description General aspects on the broad utility of AI for designing novel nano systems for drug delivery Types of formulations Vesicular nanosystems (liposomes, niosomes, and transfersome) Nanoemulsion, nanosuspension, and nanogels Polymeric and lipid nanoparticles Inorganic, metallic, and magnetic nanoparticles Polymeric micelles and dendrimers Nanosized biomaterials Nanopowder, nanocrystals, electrospun-medicated nanofibers, and quantum dots Artificial DNA nanostructures and protein nanoparticles Conclusions References AI in microfabrication technology Introduction to MEMS/NEMS devices AI in fabrication of MEMS devices Implantable microchips for programmed delivery of drugs AI in drug delivery application to the artificial pancreas Controlled drug release Microfabricated external drug reservoirs for continuous and pulsatile drug delivery Oral tablets Contact lenses Nanoemulsions Vaginal delivery AI in drug reservoirs devices AI-integrated smart biosensors in targeted delivery Microneedles Responsive polymers Conducting polymers AI in fabrication of smart biosensors Nanobots Microfluidic platforms for drug delivery Microfabrication of the particles Microfluidic platforms and smart drug delivery Janus particles Microneedles Lab-on-a-chip Organ-on-a-chip technologies in drug delivery AI in fabrication of OOC devices Drug delivery via OOC AI in drug delivery via OOC References Tracing the nose-to-brain nanoparticulate drug delivery using bio/chemoinformatics tools Rationale of nose-to-brain delivery and targeting Advantages of nose-to-brain delivery Challenges of nose-to-brain drug delivery Nanomedicine in nose-to-brain delivery Computer-assisted drug formulation design (pharmaceutics informatics=bioinformatics+chemoinformatics) Applications of bio/chemoinformatics tools in tracing and comparing the nose-to-brain delivery of different drug mo ... Bio/chemoinformatics tracing the nose-to-brain delivery of curcuminoids in the treatment of Alzheimer's disease Bio/chemoinformatics tracing the nose-to-brain delivery of cefotaxime and ceftriaxone in the treatment of meningitis The use of bio/chemoinformatics in evaluating the efficiency of targeting moieties in nose-to-brain delivery Conclusion References Applicability of machine learning in three-dimensionally (3D) printed dosage forms Introduction Classification of printers by printing mechanism Material deposition based 3D printing Fused deposition modeling/fused filament fabrication (temperature-based deposition) Semisolid extrusion 3D printing (temperature and pressure-based deposition) 3D bioprinting (pressure mediated deposition of shear-thinning inks) Material bed based 3D printing Binder jetting/material jetting (liquid binder-based building of 3D structures) Selective laser sintering/Selective laser melting (UV/N-IR laser-based building of 3D structures) Light-based photochemical cross-linking (laser/light crosslinking based building of 3D structures) Laser-based bioprinting Materials in pharmaceutical 3D printing Materials in 3D printing of small molecules Extrusion based (FDM, PAM) Powder-based (binder jetting, SLS) Materials in 3D printing of large molecules Materials used in bioprinting Bio printability Shape fidelity References Further reading Role of AI in ADME/Tox toward formulation optimization and delivery Introduction to the history of AI in ADME/Tox An in vitro-in vivo correlation Levels and the classification system of IVIVC for drugs In silico ADME/Tox profiling In silico modeling of ADME/Tox properties with descriptors Absorption modeling and physicochemical properties and descriptors Permeability Solubility Caco-2 cells Lipophilicity (Log Po/w) Intestinal absorption in humans Protein and tissue binding Distribution modeling Volume of distribution P-glycoprotein (P-gp) substrate BBB permeability Fraction unbound Metabolism modeling Excretion modeling and descriptors Total clearance (CLt) Renal clearance (CLr) Hepatic clearance (CLh) Fraction of drug excreted unchanged in urine (fe) In silico methods for predicting drug toxicity Acute toxicity Genotoxicity Systems toxicology hERG inhibition Ames toxicity Hepatotoxicity Drug toxicity Drug toxicity classifications Acute toxicity Chronic toxicity Drug-related death Drug toxicity and poisoning Pharmacological toxicity Pathological toxicity Genetic toxicity Drug toxicity mechanisms Specific toxicity or sublethal toxicity Nonspecific toxicity or lethal toxicity potential Types of therapeutic drug toxicities Dose-dependent reactions Allergic reactions Idiosyncratic reactions Drug-drug interactions Toxicokinetics Clinical pharmacology Pharmacokinetics Target discovery Drug development Drug evaluation Pharmacodynamics Artificial intelligence tools and software in ADME/Tox Machine learning algorithms in ADME/Tox Random forest Support vector machine Neural network K-nearest neighbors Naïve Bayes Deep learning References Recent advances in self-regulated drug delivery devices Introduction Strategies Nanodevices Microdevices AI-based devices Applications Diabetes Infections Anesthesia and pain relief Nervous system diseases Prospects and challenges Conclusions References Design and control of nanorobots and nanomachines in drug delivery and diagnosis Introduction Drug delivery Nanotechnology for drug delivery Nanorobots and nanomachines Design and fabrication of nanorobots and nanomachines Actuation mechanisms of nanorobots and nanomachines Nanotechnology-enabled artificial blood: Respirocytes, clottocytes, and microbivores Nanorobots and nanomachines as smart biosensors Applications of nanorobots and nanomachines in targeted drug delivery In vitro applications In vivo applications Self-driven and bioinspired nanorobots and nanomachines for drug delivery Autonomous nanorobots and nanomachines as drug delivery vehicles Biologically inspired nanorobots as drug delivery vehicles Challenges and future outlook Conclusions References Artificial intelligence (AI) in drug product designing, development, and manufacturing Introduction Impact and risk assessment of material attributes and process parameters Risk assessment of the material attributes of APIs Risk assessment of the material attributes of excipients Risk assessment of the processing parameters of manufacturing processes Design space development for CMAs and CPPs with DoE Selection of factors (CMAs/CPPs) and types of experimental designs Screening of independent factors Setting factor ranges: Levels Identifying dependent response variables Selection of a mathematical model Types of experiment designs Randomization of runs, replication, blocking, and measurement of responses (CQAs) Randomization of runs Replication Blocking Measurement and analysis of responses Numerical and graphical analyses of a mathematical regression model Numerical analysis of a mathematical regression model Graphical analysis of a mathematical regression model Development and verification of the ideal region of a design space Control space implementation for CMAs and CPPs Continuous inline/online analysis and controlling with PAT Classification of inline/online process sensors PAT tools Designing of sampling strategies and location of sensors Analyzing and controlling CMAs, CPPs, and CQAs using PAT and data science Digital twins, Internet of Things, and outlook Simulation tools in drug product designing and manufacturing process development Introduction and need for simulation tools Data-driven modeling: AI/ML/DL for product and process development Flowsheet modeling for product and process development Limitations of simulation tools in product designing and process development Data science, machine learning, and outlook Artificial intelligence in drug product commercial manufacturing and analysis AI in the drug product batch manufacturing process AI in the drug product continuous manufacturing process AI in drug delivery and drug product research and development AI in drug delivery AI in product research and development Challenges in the implementation of AI High AI implementation cost Time constraints in AI development Inadequate expertise of pharmaceutical IT teams Inadequate clarity on the correct use and implementation of AI in the existing process Proper feed of well-organized data Compliance with regulatory requirements Legal challenges associated with process Harmonization of requirements for AI implementation Future scope for the implementation of AI For compliance with regulatory requirements and quality assurance For predictive maintenance of the manufacturing line Planning of production activities Conclusions References Further reading Impact of AI on drug delivery and pharmacokinetics: The present scenario and future prospects Introduction Applications of artificial intelligence in drug delivery Significance of AI in drug delivery Artificial intelligence in the development of a drug delivery system: A research outlook Applications of artificial intelligence in pharmacokinetics Computational pharmacokinetic modeling In silico physicochemical property prediction Hydrogen bonding Lipophilicity Permeability Solubility Molecular modeling QSAR modeling ADME modeling Molecular and pharmacophore modeling Mathematical modeling Process simulation in pharmacokinetics The current status of AI in pharmacokinetics Future prospective References Artificial intelligence in vaccine development: Significance and challenges ahead Machine learning approaches in vaccine development Supervised classification in bioinformatics Proteomics Genomics Pattern recognition Employing proteomics for gonorrhea antigen mining The basic workflow of a machine learning algorithm for classification K-means clustering algorithms Requirements of clustering Logistic regression Regression approaches to the assessment of influenza vaccine effectiveness Prediction of vaccination outcomes by neural networks and logistic regression Naïve Bayesian classification Applications of Naïve Bayes algorithms Implementation of a vaccine development model Neural networks Graph convolutional neural networks Epidemic graph convolutional networks Recurrent neural networks Long short-term memory networks Deep convolutional neural networks Computational protein design using deep neural networks Design of epitope-based vaccines using deep learning Reverse vaccinology Reverse vaccinology prediction using VAXIGEN-ML Random forest analysis Support vector machine-based prediction of binding peptides Recursive feature elimination AI in the vaccine adverse event reporting system An AI-powered vaccine safety data Bank: The key to vaccine development mRNA- and protein-based vaccines in collaboration with the AI ecosystem Advanced deep Q learning network with fragment-based drug design Challenges of implementing an AI-based vaccine development model Machine learning platforms for vaccine development SIMON: Sequential iterative modeling OverNight MIT's OptiVax References AI-enabled quadrupole stimuli-responsive targeted polymeric nanodrug delivery for cancer therapy Introduction Existing pharmaceuticals Nanomedicines Properties of cancer cells The Nano4XX (XX=Dox, Cis, etc.) platform Cell intercalation Cytotoxicity Biodistribution of FA-targeted nanocontainers in HeLa tumor-bearing mice Switching effect Conclusions References Convergence of artificial intelligence and nanotechnology in the development of novel formulations for cancer ... Introduction Areas where AI is potentially implied in drug discovery and development Target identification and validation Prediction of the target protein structure Predicting drug-protein interactions Hit discovery Hit-to-lead optimization and lead optimization Lead optimization and preclinical testing Prediction of bioactivity Prediction of toxicity Clinical trials Statistical and ML methods for modeling cancer risk Common features and differences in statistical and ML models Commonly used models Statistical models ML models Supervised learning Unsupervised ML Regularized logistic regression Support vector machine Artificial neural network Preparation and optimization of nanomedicines Nanoparticles Targeted drug delivery ML and AI in formulation designing ML in nanoformulations AI in nanoformulations ML in cancer nanomedicines Application of AI and related technologies in cancer treatment using nanomedicines Big data libraries for nanomedicines AI and ML for determining the in vivo fate of nanomedicines Prediction of the in vivo behavior of nanomaterials Nanomaterials and biotoxicity prediction Data on the interplay between nanotechnology and biology ML promotes bioapplications of NMs Cancer nanomedicine: The future Conclusions and future outlook References Artificial intelligence in precision medicine Artificial intelligence and precision medicine Precision medicine AI in cancer classification and subtype determination Major AI-based methodologies Importance of structural variant detection in cancer Detection of somatic SVs in short-read WGS data Combinatorial algorithms integrate multiple read alignment patterns New PCWAG and TCGA approach to classify structural variants Histology and imaging in cancer diagnosis Solid tumors and radiographic images AI in the characterization of biomarkers Motor proteins Motor proteins and neurofilaments in neural diseases Engineered motor proteins Artificial intelligence in the structure-function analysis of kinesins AI and precision medicine in a clinical setting Commercial companies focused on AI and precision medicine Molecular structure prediction: AlphaFold Emergence of AlphaFold: Description of Google's algorithm AlphaFold: Challenges to tackle AI and precision medicine in COVID-19 research New-generation sequencing and rapid identification of SARS-COV2 variants Epidemiological models and COVID-19 severity AI in innovative diagnosis approach and analysis of cough pattern Challenges in AI Future prospects and issues in artificial intelligence and precision medicine References Artificial intelligence and machine learning in clinical trial design and application Introduction: Clinical trials in this new world History of clinical trials and innovation Gaps in clinical trials today Limited applicability Time Cost Next generation of clinical trials powered by deep technology and AI External control arm Risk models to optimize cohorts Cohort selection and optimization Patient recruitment/site recruitment Discussion Prospective ECA and retrospective comparative arms Cohort optimization Decentralized RCTs to improve health equity and representation References Artificial intelligence from a regulatory perspective: Drug delivery and devices Introduction Regulatory agencies and regulatory pathways USFDA regulatory regime [1] European regulatory regime [2] The regulatory alliances [3,4] Artificial intelligence and machine learning synergies with mission of regulatory agencies Prevailing challenges for regulatory agencies Current state of regulatory affairs and drug regulations Artificial intelligence and machine learning in drug discovery and development-Current regulatory perspective [6-8] Artificial intelligence and machine learning in medical devices-Current regulatory perspective [9-11] The future regulatory perspective Artificial intelligence and machine learning in drug discovery and development-Future perspective [14,15] Artificial intelligence and machine learning in medical devices-Future perspective [17-19] References Index A B C D E F G H I J K L M N O P Q R S T U V W X Z A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies. Focuses on the use of Artificial Intelligence in drug delivery strategies and future impacts Provides insights into how artificial intelligence can be effectively used for the development of advanced drug delivery systems Written by experts in the field of advanced drug delivery systems and digital health