Artificial Intelligence In Precision Medicine And Drug Discovery: Insights From The Recent Annual 18th Bio-IT World Conference

Artificial Intelligence In Precision Medicine And Drug Discovery: Insights From The Recent Annual 18th Bio-IT World Conference

Publication date: Apr 25, 2019

My reason for attending the conference was that, during my analysis of some biotechnology companies as investments, I came to know that many of them are using proprietary algorithms that use technologies like artificial intelligence, AI, and data analytics to find the best therapeutic targets, for example, the neoantigens from a cancer patient’s tumor biopsy, which might have the highest likelihood of eliciting T-cell response then injected in the patient as a cancer vaccine in combination with checkpoint inhibitors.

My reason for attending this conference was to learn more about how these newer technologies like AI and data analytics are being used in drug discovery and other applications in medicine.

Due to my personal interest in investing in biotechnology companies which are developing therapies for genetic diseases and cancers, I decided to attend the ‘AI for genomics’ track among approximately 20 tracks are available at this vast conference.

The use of AI and data analytics has become important in drug discovery and medicine after the concept of precision medicine i. e. treatments that are targeted at small groups that share a common feature, for example, an abnormal mutation in a type of cancer.

From among the thousands of potential targets, AI and deep learning are able to perform the data analysis much faster than humans using insights gained from different healthcare databases, including genetic markers data collected from thousands of patients.

AI can perform complex statistical calculations and correlations by analyzing this vast patient data and provide insights regarding which anticancer targets could be potential future therapies.

In the United Kingdom, the National Health Service, NHS, has been collecting genomic data from NHS patients (with a genetic disease or their families and those with cancer) and has sequenced over 100,000 genomes from 85,000 people so far.

Using AI, data from these vast genomic databases can be analyzed to gain insights like new therapeutic targets.

An interesting example of using AI/data analytics in drug development was a poster presentation from AbbVie (ABBV) which has developed an internal algorithm and process called AbbVie’s Target and Genomic algorithm, AGTC, to assist in its drug delivery efforts.

The reasons are shown in the figure below: Apart from issues like poor prediction performance, need for extensive validation, insufficient validation of many models before putting in use, difficulties in model interpretation, AI-based algorithms also have ethical and legal issues like maintaining the privacy of healthcare data.

Using the right data to validate and train AI algorithms, which is sufficiently large, has high signal to noise ratio, covers well-defined and measured clinical endpoints, and avoids biases towards specific sub-populations (e. g. based on race), may help to improve the above-mentioned challenges before the algorithm is used in a real-world setting, like a clinical trial or in clinical practice (from the above presentation by Stephen Hsu).

AI has to potential to disrupt the field of healthcare, including precision medicine, drug discovery, disease screening and diagnosis, and healthcare delivery quality improvement.

While some exciting developments have been seen, for example, the high accuracy of AI algorithms in diagnosing diabetic retinopathy or breast malignancy in pathology specimens, the AI algorithms are mostly using visual feature processing here (similar to screening for a person using his facial features).

Concepts Keywords
23andMe Example high algorithms
AbbVie Models algorithms
AI Chemotherapy
Alexa Biotechnology
Algorithm Diagnostic tests
Alzheimer Example high algorithms
Amazon Artificial intelligence
Art Collective Validate train algorithms
Artificial Intelligence Genomics
Asymptomatic Drug delivery
AUC Drug Discovery
Benign Alpha
Big Pharma DNA sequencing
Biomarkers Deep learning
Biopsy Bioinformatics
Biotech Systems biology
Biotechnology Artificial intelligence
Boston Precision medicine
Breast Cybernetics
Breast Cancer Biotechnology
Cambridge Emerging technologies
Cancer Academic disciplines
CEO Branches of biology
Chemotherapy Articles
Clinical Trial Chemotherapy
Data Analytics Antitrypsin deficiency
Deep Learning Risk score tumor
Deep Neural Networks Risks diseases
Diabetes Genetic cancer
Diabetic Retinopathy Tumor
Diagnostic Test Diabetic retinopathy
Disclosure Deep neural networks
Drug Delivery Privacy healthcare data
Drug Discovery Models algorithms
Equities Retinal imaging
FDA Claims proprietary algorithms
Financial Adviser Biotechnology
French Healthcare databases
Genetic Electronic record systems
Genetic Diseases Validate train algorithms
Genetic Disorders
Genetic Mutations
Genomic Sequencing
Genomics
Google
Gritstone
Healthcare
Histological
MA
Malignancy
Medicine
Mutation
National Service
NHS
OTCQX
Pathology
Portrait Painting
Precision Medicine
Preventive Medicine
Privacy
Seeking Alpha
Signal Noise
Spectrum
Substitute
T Cell
Tumor
United Kingdom
Vaccine
Visual Processing
Wearable Sensors

Semantics

Type Source Name
gene UNIPROT TNFRSF19
gene UNIPROT BEST1
disease MESH cancer
disease DOID cancer
drug DRUGBANK Tropicamide
disease MESH genetic diseases
disease MESH genetic markers
disease MESH diagnosis
disease MESH breast cancer
disease DOID breast cancer
pathway BSID Breast cancer
disease MESH recurrence
disease MESH diabetic retinopathy
disease DOID diabetic retinopathy
disease MESH alpha-1 antitrypsin deficiency
gene UNIPROT NHS
disease DOID genetic disease
disease MESH development
gene UNIPROT LARGE1
gene UNIPROT AMACR
gene UNIPROT AGRP
gene UNIPROT ARTN
disease MESH visual
gene UNIPROT SELL

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