Module 10: Emerging Trends in Pharmaceutical Engineering PDF

Module 10: Emerging Trends in Pharmaceutical Engineering Group 1 Antone, Mark Jairo Carabuena, Nicole Cordero, Francilene Panes, Jerry Pielago, Leah 1. List Ways Artificial Intelligence and Machine Learning are Used in D...

Module 10: Emerging Trends in Pharmaceutical Engineering Group 1 Antone, Mark Jairo Carabuena, Nicole Cordero, Francilene Panes, Jerry Pielago, Leah 1. List Ways Artificial Intelligence and Machine Learning are Used in Drug Discovery 2. Identify Example of Automation and Smart Manufacturing in the Pharmaceutical Industry 3. Define Key Terms Related to Personalized Medicine and Gene Therapy Trends Artificial Intelligence and Machine Learning in Drug Discovery Automation and Smart Manufacturing in Pharmaceutical Industry Personalized Medicine and Gene Therapy Uses of AI and Machine Learning in Drug Discovery Target Identification and Validation AI algorithms streamline the identification and validation of drug targets by analyzing genomic, proteomic, and clinical data with enhanced efficiency and accuracy. Predicting Drug Properties AI systems predict drug properties like toxicity, bioactivity, and solubility, optimizing lead compound selection and reducing the need for costly physical tests. Molecular Simulations Machine learning enables precise molecular simulations to predict drug-target interactions, reducing reliance on lab testing. De Novo Drug Design AI can generate novel drug candidates from scratch using generative models to create molecular structures with desired biological properties. Candidate Drug Prioritization AI tools rank potential drug candidates by success likelihood, helping researchers prioritize the most promising leads. Optimizing Experimental Design AI optimizes experimental design by analyzing past studies to identify ideal variables, minimizing errors, and improving result robustness. Virtual Screening AI accelerates virtual screening by rapidly evaluating large compound libraries for target binding potential, quickly identifying promising candidates. Clinical Trial Optimization AI streamlines patient recruitment and trial design by analyzing health records and past data to enroll suitable participants and predict trial success. Pharmacogenomics By integrating genomics with pharmacokinetics, AI helps in personalizing medication by predicting how individuals will respond to specific drugs based on their genetic makeup. Drug Repositioning AI can analyze existing drugs to find new therapeutic uses for them, facilitating faster pathways for drug approval and reducing the time and cost associated with developing new drugs from scratch. Automation and Smart Manufacturing in Pharmaceuticals Examples of Automation in Pharmaceutical Industry Filling and Packaging Automated Quality Control Systems Automation technologies quickly fill and These systems use cameras and sensors seal thousands of medication bottles per to inspect products for defects, ensuring hour, ensuring speed and accuracy. they meet strict quality standards before reaching the market. Robotic Mixing and Blending Robotic Medical Device Assembly Robots mix medication ingredients Robots assemble products like with high precision, ensuring syringes and inhalers, ensuring consistent batches that meet assembly is done accurately and quality standards. consistently.  Examples of Smart Manufacturing in Pharmaceutical Industry Continuous Manufacturing Digital Twins Technology Continuous manufacturing processes Creating a virtual model of a produce drugs in a steady stream, manufacturing process enables companies resulting in faster production and to simulate operations, troubleshoot reduced waste. issues, and optimize performance in real time. Predictive Maintenance Smart Factory Management Systems Smart algorithms analyze equipment data to Advanced software collects and analyzes data predict when machines might fail, allowing across the manufacturing process, offering for maintenance before breakdowns occur. insights for improving efficiency and productivity. Personalized Medicine and Gene Therapy  Key Terms in Personalized Medicine Personalized Medicine Pharmacogenomics Personalized medicine tailors treatment to Pharmacogenomics studies how genes influence individual characteristics, needs, and preferences drug responses, combining pharmacology and using genetic, biomarker, and environmental genomics to develop more effective medications information to optimize patient care and outcomes. based on genetic profiles. Biomarkers Genomics Biomarkers are measurable indicators of Genomics focuses on an organism's entire biological processes or diseases, aiding in genome and its gene interactions, helping diagnosis, predicting outcomes, and identify genetic factors that contribute to monitoring treatment responses. diseases and treatment responses.  Key Terms in Gene Therapy Gene Therapy CRISPR-Cas9 Gene therapy is a medical technique that CRISPR is a powerful genome-editing modifies a person's genes to treat or prevent technology that allows scientists to disease by adding a healthy gene or editing precisely alter DNA by cutting it at existing genes to improve their function. specific locations, enabling the addition or removal of genetic material. Vectors Somatic Gene Therapy Germline Gene Therapy Vectors are delivery systems in Somatic gene therapy alters the Germline gene therapy alters genes in gene therapy, often modified genes in body cells, with changes sperm, eggs, or embryos, resulting in viruses, that transport genetic that do not affect the germline and inherited changes that can be passed down material into cells without causing are not inherited by future to future generations and raise significant disease. generations. ethical concerns.  Mwahh Thank You Mwahh