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AI Changing Drug Discovery & Development
1. Artificial Intelligence in
Drug Discovery &
Development
Dr. Manu Kumar Shetty
Associate Professor
Department of Pharmacology
Maulana Azad Medical College
New Delhi
2. AI is changing drug discovery
Time 40min
Why we need AI
What are AI Models
Advantages
Present status &
Challenges
3. AI/ML in Drug development,
Need?
“ Evolution is not a force but a process “
High attrition rate
Increased expenditure
Increase volume of data
Increase global regulatory requirement
Challenge in timely processing
4. Next Generation Drug
Development
“We’re really at the cusp of delivering huge improvements in drug discovery
and development”
Shift from traditional model to Next-Gen
automated and intelligent model
Next Generation
Drug Development
AI/ML/DL
NLP CNN
Big Data
Analytics
5.
6. Role of AI in Drug-Discovery
Better compounds going into clinical
trials
Better target understanding
Better conductance of trials
Better Post Marketing surveillance
7. Drug Develop
1. Target protein properties
2. Ligand/drug properties
3. Target-ligand interaction
4. Drug repurposing
5. Clinical trails
6. Pharmacovigilance
18. 4). Drug Repurposing
Drug repositioning, drug retasking, drug
reprofiling, drug rescuing, drug
recycling, drug redirection, and
therapeutic switching
Process of identifying new therapeutic
use(s) for old/existing/available drugs
Highly efficient, time saving, low-cost
and minimum risk of failure, increases
the success rate
19.
20. 5). Clinical Trial
Half of time and investment
86% of all trials do not meet enrolment
timelines
1/3rd of all Phase III trials fail owing to
enrolment problems
Patient recruitment takes up 1/3rd of
duration
A 32% failure rate because of patient
recruitment problems
21. AI models used to enhance
patient cohort selection
By reducing population heterogeneity
Prognostic enrichment
Predictive enrichment
Electronic phenotyping
22. AI techniques used to automatically
analyze EMR and clinical trial eligibility
databases, find matches between
specific patients and recruiting trials,
and recommend these matches to
doctors and patients
Predict the risk of dropout for a
specific patient
23. 6). Pharmacovigilance
Insertion of structured and unstructured content: NLP
and ML are used to extract ICSR information in a
regulatory compliant manner
AI for decision-making: AI may play an important role
in predicting the new ADR
30. Advantages
Improve the quality and accuracy
Cost-effective
Timely manner
Can handle diverse types data formats
Transparent data sharing with
regulators, prescribers
Prediction of new ADR
Digital transformation has long been a buzzword in healthcare, although it is already advanced in other sector- healthcare sector is very slow adopter of new technology. Few years ago, there was little interest in automation but, today, pharma companies want to adopt tech
https://www.labiotech.eu/in-depth/ai-drug-development-covid/
With the global Covid-19 outbreak in early 2020, pharma companies and biotechs have increasingly turned to artificial intelligence to improve precision and speed in drug development.
Before COVID era- knowing AI and learning was a passion, but now it is necessitity in many indursrty.
Artificial intelligence (AI), the ability of machines to learn from new input, is a broad term for a range of computing methods. Recommendation engines used by online shopping or streaming services use forms of AI to learn consumer preferences and tailor recommendations accordingly. This same technology can be used to predict which drugs are more likely to be effective against a specific target without causing severe side effects.
AI also gives researchers the power to analyze disparate datasets. For example, it can combine vast libraries of chemical compounds, biomedical data from the literature, and patient health data into knowledge graphs. This data model creates new connections and insights into previously unrelated information, which researchers can use to make predictions, model novel pathways and disease states, and test their findings.
why- atrrition rate, cost, time , huge data, demand and expectations
The vast chemical space, comprising >1060 molecules, drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design
Despite its advantages,
a sample of 406,038 entries of clinical trial data for over 21,143 compounds from years 2000–2015, only a small percentage of substances tested are commercially successful and can be used by the pharmaceutical industry. For example, the probability of success (POS) for an orphan drug is 6.2%, and ranges from a minimum of 3.4% for oncology to a maximum of 33.4% for vaccines (infectious diseases)
Traditional PV – presently following paper and pen – manual extraction of data from various sources and analyzing.
Next Gen PV- end to end automation of repetitive manual work
Present scenario what industry experts say, what is the adoption rate
why- atrrition rate, cost, time , huge data, demand and expectations
a sample of 406,038 entries of clinical trial data for over 21,143 compounds from years 2000–2015, only a small percentage of substances tested are commercially successful and can be used by the pharmaceutical industry. For example, the probability of success (POS) for an orphan drug is 6.2%, and ranges from a minimum of 3.4% for oncology to a maximum of 33.4% for vaccines (infectious diseases)
A major reason to keep moving forward is the sheer number of signals that need to be analyzed. “Vaccines that were tested on 30,000 or 40,000 subjects in a clinical trial have now been administered to hundreds of millions of people in a matter of three or four months”, he noted. That requires safety organizations to accelerate their work as rapidly as clinical trial teams have done – and, like them, avoid returning to “the old way” of managing processes.
At the same time, the immense public awareness of clinical trials and testing surrounding COVID-19 vaccines brings another big change to PV. “Patients not only deserve to know their medicines are safe, they are now sharing data, either through self-reporting systems like the CDC’s VSafe app or via social media and other channels,” Palsulich said.
Increase in newer devices and drugs in markets increase volume of patients safety reports that are Structured and unstructured data from various sources , due to increased awareness (like social media, publications, personal devices) majority of ICSR received are unstructured content required more efforts frustration in to process 10000s of ICSR , More patient-centric data will be available from more disparate sources
Manpower alone cannot manage the increasing data volumes and complexities. This is due to increased volume of adverse events and pressure from businesses to lower their costs through automation.
Because of these reasons, traditional PV will unsustainable in near future. Therefore need new tech like for better management and better scale up drug safety activities through automated intake and
https://www.pharmexec.com/view/future-pharmacovigilance-and-regulation-management-depends-automation
More and more drug approval, public awareness, patient centric, social media
https://www.expresspharma.in/pharmacovigilance-during-a-pandemic/
bench to the bedside can be imagined given that it can aid rational drug design
better compounds going into clinical trials (related to the structure itself, but also including the right dosing/PK for suitable efficacy versus the safety/therapeutic index, in the desired target tissue);
better validated targets (to decrease the number of failures owing to efficacy, especially in clinical Phases II and III, which have a profound impact on overall project success and in which target validation is currently probably not yet where one would like it to be
better patient selection (e.g., using biomarkers
better conductance of trials (with respect to, e.g., patient recruitment and adherence)
In past- that is not required- but- bioassya- screen all drugs experiamerntally - Lock and key (differnt types of locks)
Proteins are the building blocks of life, responsible for most of what happens inside cells. How a protein works and what it does is determined by its 3D shape — ‘structure is function’ is an axiom of molecular biology. Proteins tend to adopt their shape without help, guided only by the laws of physics.
For decades, laboratory experiments have been the main way to get good protein structures. The first complete structures of proteins were determined, starting in the 1950s, using a technique in which X-ray beams are fired at crystallized proteins and the diffracted light translated into a protein’s atomic coordinates. X-ray crystallography has produced the lion’s share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.
mathematical modeling techniques that can be used to ,
It was shown by Lipinski,37 who introduced a rule of five which defines molecular properties essential for a drug's pharmacokinetics in the human body, that the chemical space might contain as many as 1060 compounds when taking into consideration only basic structural rules.
The biggest databases are GDP-13,45 containing approximately 970 million compounds, and GDP-17,46 containing 166 billion organic small molecules, both freely available for researchers
Fig. 1. The molecular property prediction flow chart. Orange dash arrows depict representations with information loss. Blue solid arrows represent the mathematical transformation without information loss. Yellow arrowsrepresent the learning process. Starting from the upper left, a molecule is composed of a group of atoms held together by chemical bonds in 3D space. Analytical chemistry techniques can be used to identify the composition of atoms and bonds in a molecule. A typical way of representing molecules is through a 2D molecular graph, aka, molecular structure. In addition, analytical measurements can also be used directly to calculate molecular descriptors. Most molecular representations start from the molecular structure that can be further converted or abstracted to SMILES, Graph Model, Fingerprint, and Descriptors. Inspired by representation learning, these molecular representations can be further converted into molecular embeddings through deep learning models. Once the molecules are converted to proper representations, machine learning models can be applied to build molecular property prediction models.
In machine learning methods, knowledge about drugs, targets and already confirmed DTIs are translated into features that are used to train a predictive model, which in turn is used to predict interactions between new drugs and/or new targets.
Patient cohort selection and recruiting mechanisms
https://www.sciencedirect.com/science/article/pii/S0165614719301300
Clinical trials are usually not designed to demonstrate the effectiveness of a treatment in a random sample of the general population, but instead aim to prospectively select a subset of the population in which the effect of the drug, if there is one, can more readily be demonstrated, a strategy referred to as 'clinical trial enrichmen.
Recruiting a high number of suitable patients does not guarantee success of a trial, but enrolling unsuitable patients increases the likelihood of its failure
AI models and methods can also be used to enhance patient cohort selection through one or more of the following means identified by the Food and Drug administration (FDA): (i) by reducing population heterogeneity, (ii) by choosing patients who are more likely to have a measurable clinical endpoint, also called 'prognostic enrichment', and (iii) by identifying a population more capable of responding to a treatment, also termed 'predictive enrichment
The Central Drugs Standards Control Organization (CDSCO) (Pharmacovigilance Gsr 287 € dated 8-03-2016, REGD.D.L.-33004/99) has made it mandatory for the MAHs to report ICSR of the marketed drug in India to National Coordination Center for Pharmacovigilance Programme of India (NCC-PvPI) as well as to them. Currently, 64,441 ICSR has been collected and submitted to NCC-PvPI, Ghaziabad. Finally, these reports will be sent to WHO-UMC, Sweden, through VigiFlow software
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6984023/#__ffn_sectitle
Various factors contribute to the ADR- dose, enviromnet -
most recent Oracle Health Sciences Connect event, April 2021
science fiction for some time. How_x0002_ever, the continued rapid growth in computer_x0002_processing power over the past two decades, the availability of large data sets and the devel_x0002_opment of advanced algorithms have driven major improvements in machine learning
The first problem is that this approach is plausible only in the case of monocausal diseases. Such cases cer- tainly do exist: for example, in the case of viral infections in which a certain proteaseis requiredfor replication or a receptor is required for cell entry. Also, target-based drug discovery has led to a significant number of approved drugs, and in particular to follow-up com- pounds when a system tends to be better understood. This approach has shown real impact [29]. However, only a minority of more- complex diseases fall into this category, leading to frequent failures in the clinic, in particular as a result of poor efficacy
The second problem is that achieving activity in a model system, such as against isolated proteins, neglects the question of whether the compound reaches its intended target site
artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare.
quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost
current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo
machine learning and artificial intelligence (AI). Although the terminology differs, what matters at the core is (i) which data are being analysed and (ii) which methods are used for this purpose.