The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

✍ Scribed by Stephanie K. Ashenden (editor)

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 247
• Category: Library

✦ Synopsis

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life.

This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics.

✦ Table of Contents

Front Matter
Copyright
Contributors
Preface
Acknowledgments and conflicts of interest
Introduction to drug discovery
The drug discovery process
Target identification
Target validation
Hit identification and lead discovery
Virtual screening
Compound libraries
High-throughput screening
Structure-based drug discovery
Fragment-based drug discovery
Phenotypic drug discovery
Natural products
Lead optimization
Modeling in lead optimization
Precision medicine
Clinical testing and beyond
References
Introduction to artificial intelligence and machine learning
Supervised learning
Unsupervised learning
Semisupervised learning
Model selection
Types of data
Other key considerations
Feature generation and selection
Censored and missing data
Dependencies in the data: Time series or sequences, spatial dependence
Deep learning
Uncertainty quantification
Bayesian inference
References
Data types and resources
Notes on data
Omics data
Genomics
Transcriptomics
Metabolomics and lipomics
Proteomics
Chemical compounds
SDF format
InChI and InChI Key format
SMILES and SMARTS format
Fingerprint format
Other descriptors
Similarity measures
QSAR with regards to safety
Data resources
Toxicity related databases
Drug safety databases
Key public data-resources for precision medicine
Resources for enabling the development of computational models in oncology
Key genomic/epigenomic resources for therapeutic areas other than oncology
Resources for accessing metadata and analysis tools
References
Target identification and validation
Introduction
Target identification predictions
Gene prioritization methods
Machine learning and knowledge graphs in drug discovery
Introduction
Graph theory algorithms
Graph-oriented machine learning approaches
Feature extraction from graph
Graph-specific deep network architectures
Drug discovery knowledge graph challenges
Data, data mining, and natural language processing for information extraction
What is natural language processing
How is it used for drug discovery and development
Where is it used in drug discovery and development (and thoughts on where it is going at the end)
References
Hit discovery
Chemical space
Screening methods
High-throughput screening
Computer-aided drug discovery
De novo design
Virtual screening
Data collection and curation
Databases and access
Compounds
Targets
Activity measurement
Cleaning collected data—Best practices
Representing compounds to machine learning algorithms
Candidate learning algorithms
Naive Bayes
k-Nearest neighbors
Support vector machines
Random forests
Artificial neural networks
Multitask deep neural networks
Future directions: Learned descriptors and proteochemometric models
Graph convolutional and message passing neural networks
Proteochemometric models
Evaluating virtual screening models
Train-test splits: Random, temporal, or cluster-based?
External validation
Prospective experimental validation
Clustering in hit discovery
Butina clustering
K-means clustering
Hierarchical clustering
References
Lead optimization
What is lead optimization
Applications of machine learning in lead optimization
Assessing ADMET and biological activities properties
Matched molecular pairs
Machine learning with matched molecular pairs
References
Evaluating safety and toxicity
Introduction to computational approaches for evaluating safety and toxicity
In silico nonclinical drug safety
Machine learning approaches to toxicity prediction
k-nearest neighbors
Logistic regression
Svm
Decision tree
Random forest and other ensemble methods
Naïve Bayes classifier
Clustering and primary component analysis
Deep learning
Pharmacovigilance and drug safety
Data sources
Disproportionality analysis
Mining medical records
Electronic health records
Social media signal detection
Knowledge-based systems, association rules, and pattern recognition
Conclusions
References
Precision medicine
Cancer-targeted therapy and precision oncology
Personalized medicine and patient stratification
Methods for survival analysis
Finding the “right patient”: Data-driven identification of disease subtypes
Subtypes are the currency of precision medicine
The nature of clusters and clustering
Selection and preparation of data
Approaches to clustering and classification
Unsupervised and supervised partitional classification
Hierarchical classification
Biclustering
Clustering trajectories and time series
Integrative analysis
Deep approaches
Validation and interpretation
Direct validation
Indirect validation
Characterization
Key advances in healthcare AI driving precision medicine
Key challenges for AI in precision medicine
References
Image analysis in drug discovery
Cells
Spheroids
Microphysiological systems
Ex vivo tissue culture
Animal models
Tissue pathology
Aims and tasks in image analysis
Image enhancement
Image segmentation
Region segmentation in digital pathology
Why is it used?
The reduction in time to build acute models compared with rule-based solutions is significant
Reduction in pathologist and scientist time doing manual aspects of annotation and analysis
Consistency of decision making (inter and intrauser error)
Feature extraction
Image classification
Limitations and barriers to using DL in image analysis
The status of imaging and artificial intelligence in human clinical trials for oncology drug development
Computational pathology image analysis
Radiology image analysis
AI-based radiomics to predict response to therapy
Protein kinase inhibitors
Chemotherapy/chemoradiotherapy
Immunotherapy
Challenges in applying radiomics to drug discovery
Clinical trial validation
Regulatory approval
Distribution and reimbursement
Conclusion
Future directions
Imaging for drug screening
Computational pathology and radiomics
References
Clinical trials, real-world evidence, and digital medicine
Introduction
The importance of ethical AI
Clinical trials
Site selection
Recruitment modeling for clinical trials
Recruitment start dates
The Poisson Gamma model of trial recruitment
Nonhomogeneous recruitment rates
Applications of recruitment modeling in the clinical supply chain
Clinical event adjudication and classification
Identifying predictors of treatment response using clinical trial data
Real-world data: Challenges and applications in drug development
The RWD landscape
Barriers for adoption of RWD for clinical research
Data quality
Interoperability
Use of RWE/RWD in clinical drug development and research
Concluding thoughts on RWD
Sensors and wearable devices
Sample case study: Parkinson’s disease
Standards and regulations and concluding thoughts
Conclusions
References
Beyond the patient: Advanced techniques to help predict the fate and effects of pharmaceuticals in the environment
Overview
Background
Current European and US legislation for environmental assessment of pharmaceuticals
Animal testing for protecting the environment
Issues for database creation
Opportunities to refine animal testing for protecting the environment
Current approaches to predicting uptake of pharmaceuticals
What makes pharmaceuticals special?
Why do pharmaceuticals effect wildlife?
What happens in the environment?
Predicting uptake using ML
Regional issues and the focus of concern
Intelligent regulation—A future state of automated AI assessment of chemicals
Key points for future development
References
Index
A
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D
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J
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N
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P
Q
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T
U
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W
X
Y