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.
Author(s): Anil K. Philip, Aliasgar Shahiwala, Md. Faiyazuddin
Publisher: Academic Press/Elsevier
Year: 2023
Language: English
Pages: 623
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
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