Role of AI in Drug Discovery and Development
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Role of artificial intelligence in drug discovery and development From Lead selection, clinical trails, pharmacovigilance
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Role of AI in Drug Discovery and 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? โ Evolution is not a force but a process โ High attrition rate Increased expenditure Increase volume of data Increase global regulatory requirement Challenge in timely processing
- 4. Next Generation Drug Development โWeโre really at the cusp of delivering huge improvements in drug discovery and developmentโ Shift from traditional model to Next-Gen automated and intelligent model Next Generation Drug Development AI/ML/DL NLP CNN Big Data Analytics
- 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
- 8. 1) Target protein understanding The structure of the target protein is vital to design the drug molecule.
- 10. 2. Drug/Ligand properties prediction Quantitative structureโactivity relationship (QSAR) -predict physicochemical properties and biological activities based on their known chemical structure
- 13. 3) Drug- Target interaction (DTI) Bioassay Docking Simulation Machine Learning methods
- 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
- 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
- 24. NLP Example 1 - input
- 26. NLP Example 2 - input
- 27. Normalize drug names and dosage units with Spark NLP
- 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
- 31. Present status
- 34. Challenges Not a monocausal diseases Significant data challenges, such as the scale, growth, diversity, and uncertainty of the data.
- 35. Thank you
Editor's Notes
- 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 2000โ2015, 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 โ presently following paper and pen โ 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 2000โ2015, 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. โVaccines 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 monthsโ, he noted. That requires safety organizations to accelerate their work as rapidly as clinical trial teamsย have done โ and, like them, avoid returning to โthe old wayโ 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. โPatients not only deserve to know their medicines are safe, they are now sharing data, either through self-reporting systems like the CDCโsย VSafeย app or via social media and other channels,โ 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/
- https://www.pharmavoice.com/article/2020-10-pharmacovigilance/
- 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 โ โstructure is functionโ 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 proteinโs atomic coordinates. X-ray crystallography has produced the lionโs share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.
- https://www.nature.com/articles/d41586-020-03348-4 https://www.forbes.com/sites/robtoews/2021/10/03/alphafold-is-the-most-important-achievement-in-ai-ever/?sh=1c89a7e06e0a https://www.nature.com/articles/s41586-021-03828-1
- mathematical modeling techniques that can be used to , It was shown by Lipinski,37ย 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 1060ย compounds when taking into consideration only basic structural rules. The biggest databases are GDP-13,45ย containing approximately 970 million compounds, and GDP-17,46ย 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.
- https://www.synapse.org/#!Synapse:syn25958848/wiki/611610
- https://github.com/ManuHCI/Chemoinformatics/tree/master/MIT_Hack4Rare
- 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 โฌ 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 -
- https://nexocode.com/blog/posts/artificial-intelligence-for-pharmacovigilance/
- https://www.oracle.com/us/industries/health-sciences/address-data-challenges-pharma-wp-5018953.pdf
- most recent Oracle Health Sciencesย 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.