Current and Future Aspects of AI In Drug Discovery Design and Development.pptx

Current and Future Aspects of AI In
Drug Discovery Design and
Development
Prof. Dr. Basavaraj Nanjwade M. Pharm., PhD
Assist. Vice President and Plant Head
KJD Pharmaceuticals Pvt Ltd
BIDAR-585402
11/23/2021 1
Acharya & BM Reddy College of Pharmacy, Bengaluru-560090

CONTENT
• AI in Drug Discovery
• AI in Drug Design
• AI in Drug Development
• AI in Clinical Trial
• AI in Pharmaceutical manufacturing
• AI in QA & QC
• AI in Pharmaceutical Marketing
• AI in Disease Diagnosis
• AI in Disease Prevention
• AI in Epidemic Prediction
• AI in Remote Monitoring
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Artificial Intelligence
• Artificial Intelligence (AI) plays a pivotal role in drug
discovery.
• In particular artificial neural networks such as deep neural
networks or recurrent networks drive this area.
• Numerous applications in property or activity predictions
like physicochemical and ADMET properties have
recently appeared and underpin the strength of this
technology in quantitative structure-property relationships
(QSPR) or quantitative structure-activity relationships
(QSAR).
11/23/2021 3

11/23/2021 Acharya & BM Reddy College of Pharmacy, Bengaluru-560090 4
Artificial Intelligence

AI in Drug Discovery
• Classical pharmacology drug discovery
• Reverse pharmacology drug discovery
• Target based drug discovery
• Polypharmacology
• Chemical synthesis
• Drug repurposing
• Drug screening
11/23/2021 5

AI in Drug Discovery

Future Aspects of AI
Develop Assay Develop Assay
Apoptosis Assay Drug Induced Liver Assay
Autophagy Assay Endocrine Profiling Assay
Cell Cycle Assay In situ Hybridization Assay
Cell Health Profiling Assay Invasion Assay
Cell spreading Assay Micronucleus Assay
Cell Toxicity Assay Micronucles Genotoxicity Assay
Cell Viability Assay Mitotic Index Assay
Colony Formation Assay Multiple Target Translocation Assay
Comet Assay Neurite Detection Assay
Comet Genotoxicity Assay Neurotoxicity Profiling Assay
Cytoskeletal Rearrangement Assay Organelle Health Assay
DNA Damage Assays Proliferation and Cell Death Assay
Receptor Internalization Assay Single Target Translocation Assay
Synaptogenesis Assay Transfection Efficiency Assay

Classical pharmacology drug discovery
• Classical pharmacology, also known as forward
pharmacology or phenotypic drug discovery (PDD).
• Chemical libraries of synthetic small molecules.
• Natural products or extracts to identify substances
that have desirable therapeutic effect.
https://www.chembridge.com
11/23/2021 8

Reverse pharmacology drug discovery
• Reverse pharmacology is the science of
integrating documented clinical/experiential
hits, into leads by transdisciplinary exploratory
studies and further developing these into drug
candidates by experimental and clinical
research.
11/23/2021 9

Target based drug discovery
• The approach is not limited to small-molecule drug
discovery.
• Antibody drugs and other protein biologics are
generally discovered through target-based
approaches, and
• The discovery of gene therapy and nucleic acid-based
therapeutics is inherently target based as well.
11/23/2021 10

Classical pharmacology and Target Based
Drug Discovery
Target-based approach Phenotypic approach
Cost Lower Higher
Speed Rapid Moderate
Ease of SAR Easier More difficult
Translatability Depends on other evidence Presumed higher in general
Molecular Target Known May not be identified

Polypharmacology
• Polypharmacology is the design or use of
pharmaceutical agents that act on multiple targets or
disease pathways.
• Despite scientific advancements and an increase of
global R&D spending, drugs are frequently
withdrawn from markets.
• This is primarily due to their side effects or toxicities.
• Designing biospecific drug molecules
• Designing multitarget drug molecule
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Designing biospecific drug molecules
• Antibodies (Abs) containing two different antigen-
binding sites in one molecule are called bispecific.
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Designing multitarget drug molecule
• The use of multitarget drugs is promising as it lowers
the possibility of the disease to evolve into a drug-
resistant phenotype.
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Designing multitarget drug molecule

Chemical synthesis
• Prediction of reaction yield
• Prediction of retrosynthesis pathways
• Developing insights into reaction mechanisms
• Designing synthetic route
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Prediction of reaction yield
11/23/2021 17
Chemical syntheses in terms of resource and energy
efficiency, product selectivity, operational simplicity, and
health and environmental safety.

Prediction of retrosynthesis pathways
• Retrosynthetic pathways is a technique for solving
problems in the planning of organic syntheses.
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Developing insights into reaction
mechanisms
• Reaction mechanism, or reaction pathway, describes
the successive steps at the molecular level that take
place in a chemical reaction.
• In each step, molecular bonds are either created or
broken.

Designing synthetic route
• The simplest synthesis of a molecule is one in
which the target molecule can be obtained by
using a readily available starting material for a
single reaction that converts it to the desired target
molecule.
11/23/2021 20

Drug repurposing
• Identification of therapeutic target
• Prediction of new therapeutic use
11/23/2021 21

Identification of therapeutic target
• Identifying a biological target that is ‘druggable’ – a target is
termed ‘druggable’ if its activity (behavior or function) can be
modulated by a therapeutic – whether it be a small molecule
drug, or biologic.
• Proteins and nucleic acids are both examples of biological
targets.
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• The target has a confirmed role in the pathophysiology of a
disease and/or is disease-modifying.
• Target expression is not evenly distributed throughout the
body.
• The target’s 3D-structure is available to assess druggability.
• The target is easily ‘assayable’ enabling high-throughput
screening.
• The target possesses a promising toxicity profile, potential
adverse effects can be predicted using phenotypic data.
• The proposed target has a favorable intellectual property (IP)
status. (relevant for pharma companies)
Identification of therapeutic target

Prediction of new therapeutic use
• Machine-learning methods are particularly suited to
predictions based on existing data, but precise
predictions about the distant future are often
fundamentally impossible.
11/23/2021 24

Drug screening
• Prediction of toxicity
• Prediction of bioactivity
• Prediction of physicochemical property
• Identification and classification of target cells
11/23/2021 25

Prediction of toxicity
• The prediction of the toxicity of any drug molecule is vital to avoid toxic
effects.
• Cell-based in vitro assays are often used as preliminary studies, followed
by animal studies to identify the toxicity of a compound, increasing the
expense of drug discovery. Several web-based tools, such as LimTox,
pkCSM, admetSAR, and Toxtree, are available to help reduce the cost
11/23/2021 26

Prediction of bioactivity
• The efficacy of drug molecules depends on their
affinity for the target protein or receptor.
• Drug molecules that do not show any interaction or
affinity towards the targeted protein will not be able
to deliver the therapeutic response.
11/23/2021 27

Prediction of physicochemical property
• Physicochemical properties, such as solubility,
partition coefficient (logP), degree of ionization, and
intrinsic permeability of the drug, indirectly affect its
pharmacokinetics properties and its target receptor
family and, hence, must be considered when
designing a new drug.
• Different AI-based tools can be used to predict
physicochemical properties.
11/23/2021 28

Identification and classification of target cells
• Target identification begins with identifying the
function of a possible therapeutic target
(gene/protein) and its role in the disease.
• Target cells are erythryoctes with an increased cell
membrane-to-volume ratio, due either to gain of
membrane lipids or to a reduction in cell volume.
11/23/2021 29

AI tools for Drug Discovery
• DeepChem: https://github.com/deepchem/deepchem
• DeepTox: www.bioinf.jku.at/research/DeepTox
• DeepNeuralNetQSAR: https://github.com/Merck/DeepNeuralNet-
QSAR
• ORGANIC: https://github.com/aspuru-guzik-group/ORGANIC
• PotentialNet: https://pubs.acs.org/doi/full/10.1021/acscentsci.8b00507
• Hit Dexter: http://hitdexter2.zbh.uni-hamburg.de
• DeltaVina: https://github.com/chengwang88/deltavina
• Neural graph fingerprint: https://github.com/HIPS/neural-fingerprint
• AlphaFold: https://deepmind.com/blog/alphafold
• Chemputer: https://zenodo.org/record/1481731
11/23/2021 30

AI in Drug Design
• Computer aided drug design
• Structure based drug design
• Predicting 3D structure of target protein
• Predicting drug-protein interactions
• Determining drug activity
• De novo drug design
11/23/2021 31

Computer aided drug design (CADD)
• CADD helps scientists in minimizing the synthetic
and biological testing efforts by focussing only on the
most promising compounds.
• Identification and optimization of new drugs using
leverage of chemical and biological information
about targets and/or ligands.
11/23/2021 32

Structure based drug design
• Once a target has been identified, it is necessary to
obtain accurate structural information.
• There are three primary methods for structure
determination that are useful for drug design: X-ray
crystallography, NMR, and homology modeling.
• The evaluation of structures from each method will
be discussed.
11/23/2021 33

Predicting 3D structure of target protein
• Currently, the main techniques used to determine
protein 3D structure are X-ray crystallography and
nuclear magnetic resonance (NMR).
• In X-ray crystallography the protein is crystallized
and then using X-ray diffraction the structure of
protein is determined.
11/23/2021 34

Predicting drug-protein interactions
• Drug-Protein Interaction, In order for a drug to act
upon a receptor or be metabolized by an enzyme it
must first be bound to receptor protein or enzyme.
11/23/2021 35

Determining drug activity
• Physiologic differences between men and women affect
drug activity, including pharmacokinetics and
pharmacodynamics.
• Pharmacokinetics in women is affected by lower body
weight, slower gastrointestinal motility, less intestinal
enzymatic activity, and slower glomerular filtration rate.
• Because of delayed gastric emptying, women may need to
extend the interval between eating and taking medications
that must be absorbed on an empty stomach.
11/23/2021 36

De novo drug design
• In de novo design, to identify amino acid sequences
folding into proteins with desired functions is the
ultimate objective.
• There are various approaches predicting protein
structure including comparative modeling and fold
recognition. In proteins, sequence similarity implies
structural similarity.
11/23/2021 37

AI in Drug Development
• QbD
• GMP
• cGMP
• Stability issues
• Dissolution
• Porosity
11/23/2021 38

• AI can assist in selecting only a specific
diseased population for recruitment in Phase II
and III of clinical trials by using patient-
specific genome–exposome profile
analysis,which can help in early prediction of
the available drug targets in the patients
selected.
AI in Clinical Trial

AI in Clinical Trial
• Phase I
• Phase II
• Phase III
• Phase IV
• Subject enrolments/selection
• Patient dropout
• Monitoring of trial
11/23/2021 40

Subject enrolments/selection
• The enrolment of patients takes one-third of the
clinical trial timeline.
• The success of a clinical trial can be ensured by the
recruitment of suitable patients, which otherwise
leads to 86% of failure cases.
11/23/2021 41

Patient dropout
• Drop out of patients from clinical trials accounts for
the failure of 30%of the clinical trials, creating
additional recruiting requirements for the completion
of the trial, leading to a wastage of time and money.
11/23/2021 42

Monitoring of trial
• This can be avoided by close monitoring of the
patients and helping them follow the desired
protocol of the clinical trial
11/23/2021 43

AI in pharmaceutical manufacturing
• Automated manufacturing
• Personalized manufacturing
• Correlating manufacturing errors to set
parameters
• Quality control
• Predictive maintenance
• Waste reduction
• Design optimization
• Process automation
11/23/2021 44

Automated manufacturing
• The pharmaceutical industry is increasingly making
use of robotics to automate specific processes in drug
development, including drug screening, anti-
counterfeiting and manufacturing tasks.
• This allows for the study of specific interactions
between a particular drug and its protein target at the
atomic level.
11/23/2021 45

Personalized manufacturing
• Custom manufacturing is the process of
designing, engineering, and producing goods
based on a customer's unique specifications,
including build to order (BTO) parts, one-offs,
short production runs, as well mass
customization.
11/23/2021 46

Correlating manufacturing errors to set
parameters
• Additive Manufacturing (AM) is a process that is
based on manufacturing parts layer by layer in order
to avoid any geometric limitation in terms of creating
the desired design.
• In the early stages of AM development, the goal was
just creating some prototypes to decrease the time of
manufacturing assessment.
11/23/2021 47

Quality control
• The lack of oversight and proper quality control
imposes severe repercussions for any drug
manufacturer and their customers.
• The chance of contamination puts customers at risk
for potentially life-threatening allergic reactions.
• The impact of those errors can result in litigation,
reputational harm, and FDA regulatory action.
11/23/2021 48

Predictive maintenance
• Predictive maintenance refers to the use of data-
driven, proactive maintenance methods that are
designed to analyze the condition of equipment and
help predict when maintenance should be performed.
11/23/2021 49

Waste reduction
• Waste reduction (or prevention) is the preferred
approach to waste management because waste that
never gets created doesn't have waste management
costs.
• An example of waste reduction is reducing
unnecessary packaging from manufactured products
and produce.
11/23/2021 50

Design optimization
• Design optimization is an engineering
design methodology using a mathematical formulation
of a design problem to support selection of the optimal
design among many alternatives.
1. Variables: Describe the design alternatives
2. Objective: Elected functional combination of variables
(to be maximized or minimized)
3. Constraints: Combination of Variables expressed as
equalities or inequalities that must be satisfied for any
acceptable design alternative
4. Feasibility: Values for set of variables that satisfies all
constraints and minimizes/maximizes Objective.
11/23/2021 51

Process automation
• Robotic process automation (RPA) software
automatically handles manual, repetitive, time-
consuming, and highly structured tasks such as data
entry and back-office functions.
11/23/2021 52

Quality Assurance and Control
• Understand critical process parameters
• Guide future production cycle
• Regulation of in-line quality
• Ensure QA with aid of ELN and other techniques
• In order to simplify the effort of visual error detection
in the productive pharmaceutical industry and to
reduce error rates, elunic developed a system for
automatic quality assurance with the help of deep
learning algorithms.
11/23/2021 53

Understand critical process parameters
• A Critical Process Parameter (CPP) is a term used in
pharmaceutical production for process variables
which have an impact on a critical quality attribute
(CQA) and, therefore, should be monitored or
controlled to ensure the drug product obtains the
desired quality.
11/23/2021 54

Guide future production cycle
11/23/2021 55

Regulation of in-line quality
• The in-line quality control process
incorporates inspection points across the production
line.
• These points inspect the product for quality in terms
of various standards or specifications.
• Process control
• Control charts
• Product quality control,
• Process control
11/23/2021 56

Ensure QA with aid of ELN and other
techniques
• Electronic Lab Notebooks (ELN),
• Laboratory Information Management Systems (LIMS),
• Chromatography Data Systems (CDS),
• and Laboratory Execution Systems (LES).
11/23/2021 57

AI in Pharmaceutical Marketing
• Market positioning
• Market prediction and analysis
• Product costing
11/23/2021 58

Market positioning
• Positioning refers to the place that a brand occupies in
the minds of the customers and how it is
distinguished from the products of the competitors
and different from the concept of brand awareness.

Market prediction and analysis
• Technical analysis focuses on analyzing historical
stock prices to predict future stock values (i.e. it
focuses on the direction of prices).
• A market analysis is a quantitative and qualitative
assessment of a market.
• It looks into the size of the market both in volume
and in value, the various customer segments and
buying patterns, the competition, and the economic
environment in terms of barriers to entry and
regulation.

Product costing
• Product cost refers to the costs incurred to create a
product.
• These costs include direct labor, direct materials,
consumable production supplies, and factory
overhead.
• Product cost can also be considered the cost of the
labor required to deliver a service to a customer.

AI in Disease Diagnosis
• Doctors can use advanced Machine Learning systems to
collect, process, and analyze vast volumes of patients’
healthcare data.
• Healthcare providers around the world are using ML
technology to store sensitive patient data securely in the cloud
or a centralized storage system.
• This is known as electronic medical records (EMRs).
• ML systems can use the data stored in EMRs to make real-
time predictions for diagnosis purposes and suggest proper
treatment to patients.
11/23/2021 62

AI in Disease Prevention
• Pharma companies can use AI to develop cures for
both known diseases like Alzheimer’s and
Parkinson’s and rare diseases.
•
• Generally, pharmaceutical companies do not spend
their time and resources on finding treatments for rare
diseases since the ROI is very low compared to the
time and cost it takes to develop drugs for treating
rare diseases.
11/23/2021 63

AI in Epidemic Prediction
• AI and ML are already used by many pharma
companies and healthcare providers to monitor and
forecast epidemic outbreaks across the globe.
• Such AI/ML models become especially useful for
underdeveloped economies that lack the medical
infrastructure and financial framework to deal with an
epidemic outbreak.
11/23/2021 64

AI in Remote Monitoring
• Telemedicine consults, remote monitoring devices,
and AI are all evolving to provide healthcare teams
new ways to gain deeper levels of patient information
that enhance diagnosis and treatment decisions, while
also fostering client relationships.
11/23/2021 65

Pharmaceuticals with AI Organizations
Drug Discovery Today Volume 26, Number 1 January 2021

Artificial Intelligence Software
AI Tools Functionality
Google Cloud Machine Learning
Azure Machine Learning
TensorFlow Machine Learning
H2O AI Machine Learning
Cortana Virtual Assistant
IBM Watson Question-answering system.
Infosys Nia Machine Learning Chatbot.
Amazon Alexa Virtual Assistant
Google Assistant Virtual Assistant

THANK YOU
E-mail: nanjwadebk@gmail.com
Mob#: 0091 9742999277

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