Drug Discovery and Drug Repurposing.pptx

Drug Discovery vs. Drug Repurposing and AI in Pharmaceutical Manufacturing
Use of AI tools in manufacturing.

1. Drug Discovery vs. Drug Repurposing and AI in Pharmaceutical Manufacturing

2. New Drug Discovery Drug Repurposing

3. Drug Discovery: Drug discovery is the process of identifying new candidate medications. It involves several steps Finding a molecule (often a protein) that plays a key role in a disease. Target Identification Discovering a molecule that can interact with the target to modulate its activity. Optimization Refining the lead compound to improve its efficacy, reduce side effects, and enhance its pharmacokinetic properties. Preclinical Testing Conducting laboratory and animal studies to gather preliminary efficacy, toxicity, and pharmacokinetic information. Clinical Trials Testing the drug in humans through Phase I (safety), Phase II (efficacy), and Phase III (large-scale efficacy and safety) trials. Lead Compound Identification Regulatory Approval Submitting data to regulatory bodies (e.g., FDA, EMA) for approval to market the drug. Drug discovery takes time (12-17 years) and millions of dollars.

4. Drug repurposing can significantly reduce the time (3-7 years) and cost (a fraction of new drug development) compared to traditional drug discovery. Drug Repurposing: Drug repurposing (or repositioning) involves finding new therapeutic uses for existing drugs. The steps include: Using existing drugs that have already been approved or failed for other indications. Identification of Candidates Leveraging biological understanding, literature, and databases to propose new uses for the drug In vitro and In vivo Testing Testing the drug in disease models to evaluate the new potential use Clinical Trials Often, these can start at later phases (e.g., Phase II) if sufficient safety data exists from previous us Regulatory Pathways Often expedited since much is already known about the drug's safety profile Hypothesis Generation

5. Drug Discovery: Utilization of AI in drug discovery AI can analyze vast amounts of biological data, including genomics, proteomics, and transcriptomics, to identify potential drug targets. Machine learning algorithms can identify patterns and associations between genes, proteins, and diseases that might not be obvious through traditional methods. Target Identification AI algorithms can screen large chemical libraries to find molecules that can bind to the identified targets. Virtual screening with machine learning models can predict which compounds are most likely to be effective. Optimization AI-driven molecular modeling can optimize lead compounds by predicting their interactions with targets, improving efficacy, and reducing side effects. Machine learning models can suggest modifications to the chemical structure to enhance the drug's properties. Preclinical Testing AI can predict the toxicity and pharmacokinetics of drug candidates using data from in vitro and in vivo studies. Machine learning models can analyze this data to prioritize candidates with the best safety and efficacy profiles for further development. Clinical Trials AI can optimize the design of clinical trials, select appropriate patient populations, and monitor trials in real-time to ensure safety and efficacy. AI can also analyze patient data to identify biomarkers that predict response to treatment. Lead Compound Identification Regulatory Approval AI can streamline the preparation and submission of regulatory documents by automating the data analysis and report generation processes. IBM Watson Atomwise Exscientia Insilico Medicine Trials.ai ArisGlobal SparkBeyond AI Tools

6. Drug Repurposing: Utilization of AI in drug repurposing AI can analyze existing drug data to identify candidates for repurposing. Machine learning models can predict new therapeutic uses for approved drugs by identifying molecular similarities and shared pathways between different diseases. Identification of Candidates AI can mine scientific literature, clinical trial data, and biological databases to generate hypotheses about new uses for existing drugs. AI can identify potential mechanisms of action and suggest new therapeutic applications. In vitro and In vivo Testing AI can design and interpret preclinical experiments, predicting the success of repurposed drugs in disease models. Machine learning models can help prioritize experiments by predicting which drug-disease combinations are most likely to succeed. Clinical Trials AI can optimize the design and management of clinical trials for repurposed drugs, similar to new drug discovery. AI can help in identifying patient subgroups most likely to benefit from the repurposed drug, thus enhancing the trial's success rate. Hypothesis Generation Regulatory Approval AI can assist in the regulatory approval process for repurposed drugs by automating the preparation of submissions and ensuring compliance with regulatory requirements. AI can also help in identifying the fastest regulatory pathways based on the drug's existing data. BenevolentAI Healx DeepChem Trials.ai Antidote Technologies Saama Technologies ArisGlobal SparkBeyond AI Tools

7. Lab scale batch Literature Review Pre formulation development Analytical Method development stability Regulatory Approval Pilot Scale batch Market Manufacturing of Pharmaceutical Product