COMPUTATIONAL MODELING IN DRUG DISPOSITION.pptx

Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics, and computer science. It is a powerful tool that can be used to understand and predict how systems behave, without having to conduct physical experiments.

One way to think about computational modeling is to imagine a virtual world that you can create and control. You can use this virtual world to test different scenarios and see how the system behaves under different conditions.

For example, you could create a computational model of a weather system to predict how a hurricane is going to develop or, you could create a computational model of a drug to predict how it will interact with the human body.

Data Collection - Collecting experimental data on drug properties and interactions.

Model Development - Building mathematical models that represent drug behavior in the body.

Model Validation - Ensuring that models accurately predict real-world outcomes.

Model Application - Using models for various purposes like drug design, dose optimization, and clinical trial simulations.

1. COMPUTATIONAL MODELLING OF DRUG DISPOSITION PRESENTED BY: MOHD AZHAR M.PHARM (PHARMACEUTICS) GUIDED BY: DR. MEENAKSHI BHARKATIYA

2. COMPUTATIONAL MODELING Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics, and computer science. It is a powerful tool that can be used to understand and predict how systems behave, without having to conduct physical experiments . One way to think about computational modeling is to imagine a virtual world that you can create and control. You can use this virtual world to test different scenarios and see how the system behaves under different conditions . For example, you could create a computational model of a weather system to predict how a hurricane is going to develop or, you could create a computational model of a drug to predict how it will interact with the human body.

3. WHY COMPUTATIONAL MODELING? Reduces cost Minimizes animal testing Tailors treatments to individuals Speeds up drug development S R M T

4. COMPONENTS OF COMPUTATIONAL MODELING Data Collection - Collecting experimental data on drug properties and interactions . Model Development - Building mathematical models that represent drug behavior in the body . Model Validation - Ensuring that models accurately predict real-world outcomes . Model Application - Using models for various purposes like drug design, dose optimization, and clinical trial simulations.

5. TYPES OF MODELS 1. Pharmacokinetic (PK) Models - Describes drug concentration changes over time, including absorption and distribution. - Examples : One-compartment model, physiologically-based PK (PBPK) model . 2. Pharmacodynamic (PD) Models - Relates drug concentration to therapeutic effect. - Example : Emax model . 3. Quantitative Structure-Activity Relationship (QSAR) Models - Predicts biological activity based on chemical structure . 4. Systems Biology Models - Simulates complex biological processes, including metabolism. - Example : Cellular automaton models.

6. MODELLING TECHNIQUES QUANTITATIVE APPROACH 01 QUALITATIVE APPROACH 02

7. QUANTITATIVE APPROACHES Represented by pharmacophore modeling and flexible docking studies investigate the structural requirements for the interaction between drugs and the targets that are involved in ADMET process. Useful for accumulation of knowledge against a certain target. For example, a set of drugs known to be transported by a transporter would enable a pharmacophore study to elucidate the minimum required structural features for transport.

8. QUALITATIVE APPROACHES Represented by quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR ) studies utilize multivariate analysis to correlate molecular descriptors with ADMET related properties . A diverse range of molecular descriptors can be calculated based on the drug structure . I mportant to select the molecular descriptors that represent the type o f interactions contributing to the targeted biological property .

9. E ssential to select the right mathematical tool for most effective ADMET modelling sometimes it is possible to apply multiple statistical methods and compare the result to identify the best approach. A wide selection Of statistical algorithms is available to researchers for correlating field descriptors with ADMET properties including simple multiple linear regression (MLR), multivariate partial least-squares (PIS), and the nonlinear regression-type algorithms such as artificial neural networks (ANN) and support vector machine (SVM).

10. DRUG ABSORPTION Oral administration is the most preferred drug delivery form due to convenience and patient compliance . Much attention in I n- silico approaches focuses on modeling drug oral absorption, primarily occurring in the human intestine . Drug bioavailability and absorption result from the interplay between drug solubility and intestinal permeability.

11. SOLUBILITY In silico modeling predicts solubility even before synthesis . Solubility estimation: LogP value (partition coefficient) and melting point . Two modeling approaches: physiological processes-based and empirical (QSPR ). Empirical models use multivariate analysis to correlate molecular descriptors with solubility . Accurate selection of descriptors and understanding of the dissolution process are crucial .

12. INTESTINAL PERMEATION Intestinal permeation allows drugs to cross the gut mucosa into the portal circulation . Essential for drugs to reach systemic circulation and target sites . Involves passive diffusion and active transport . Complex process challenging to predict based solely on molecular mechanisms . Models simulate in vitro membrane permeation using Caco-2, MDCK, or PAMPA.

13. DISTRIBUTION Distribution is crucial in a drug's pharmacokinetic profile . Determined by structural and physiochemical properties . Reflects in VD (Volume of Distribution ). VD predicts drug half-life and dosing frequency . Models for VD prediction based solely on computed descriptors are under development.

14. DRUG EXCRETION Drug excretion is quantified by plasma clearance, clearing plasma volume free of drug per unit time . Hepatic and renal clearances are the main components . No models predict plasma clearance solely from computed drug structures . Current efforts focus on estimating in vivo clearance from in vitro data . Complexities arise from active transporters in hepatic and renal clearance processes.

15. CHALLENGES AND LIMITATIONS Data Quality Reliable ADMET data is crucial for accurate modeling. Validation and Verification Ensuring models reflect real-world ADMET behavior. Model Complexity Complex ADMET processes require sophisticated models. Regulatory Acceptance Establishing regulatory guidelines for model use, especially in ADMET predictions.

16. FUTURE DIRECTIONS HOW WE DO IT 02 Combining models at different biological scales, including ADMET, for a holistic view. Multi-scale Modeling 03 Leveraging emerging technologies for better ADMET data. Advancements in Data Collection 04 Creating standardized guidelines for ADMET model acceptance. Regulatory Framework Development 01 Integration of AI and Machine Learning Enhancing ADMET modeling capabilities with advanced algorithms.

17. CONCLUSION Computational modeling plays a pivotal role in understanding and predicting drug absorption and disposition . In silico approaches aid in early-stage drug development, optimizing oral drug delivery, and minimizing experimental work . It enhances our understanding of drug behavior in the body, including ADMET properties . Despite challenges, ongoing advancements in computational modeling continue to enhance our ability to design better drugs

18. THANK YOU FOR YOUR ATTENTION. Any questions or comments?