11.4 ADMET prediction

ADMET prediction is crucial in drug discovery, assessing pharmacokinetics and safety of potential drugs. It helps optimize lead compounds, reduce failures, and increase clinical success. By evaluating absorption, distribution, metabolism, excretion, and toxicity early on, researchers can select compounds with better profiles.

The process involves various methods, from in vitro assays to computational models. These tools estimate how drugs interact with the body, predicting properties like oral absorption, tissue distribution, and metabolic stability. Accurate ADMET prediction can significantly improve drug development efficiency and success rates.

Importance of ADMET prediction

• ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction plays a crucial role in the drug discovery and development process by assessing the pharmacokinetic and safety profiles of drug candidates
• Early prediction of ADMET properties helps optimize lead compounds, reduce attrition rates, and minimize the risk of failure in later stages of development
• Integrating ADMET prediction into the drug discovery workflow enables the selection of compounds with favorable pharmacokinetic and safety profiles, ultimately increasing the likelihood of clinical success

Role in drug discovery

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• ADMET prediction is applied in the early stages of drug discovery to identify and prioritize compounds with desirable pharmacokinetic and safety properties
• It aids in the selection of lead compounds by evaluating their potential for oral absorption, distribution to target tissues, metabolic stability, and elimination from the body
• ADMET prediction helps guide structural modifications to optimize the pharmacokinetic profile and minimize potential toxicity issues

Impact on clinical success

• Poor ADMET properties are a major cause of drug failure in clinical trials, leading to increased costs and delayed development timelines
• By identifying and addressing ADMET liabilities early in the drug discovery process, the risk of clinical failure due to pharmacokinetic or safety issues can be significantly reduced
• Compounds with favorable ADMET profiles have a higher probability of demonstrating efficacy and safety in clinical studies, increasing the chances of successful drug development and regulatory approval

Absorption prediction

• Absorption prediction focuses on estimating the extent and rate at which a drug is absorbed from the site of administration into the systemic circulation
• It involves evaluating the physicochemical properties, permeability, and bioavailability of a drug candidate to assess its potential for oral absorption
• Accurate absorption prediction is crucial for determining the appropriate route of administration and optimizing the formulation strategy

Physicochemical properties

• Physicochemical properties such as molecular weight, lipophilicity (LogP), hydrogen bond donors/acceptors, and polar surface area (PSA) influence drug absorption
• These properties affect the solubility, permeability, and membrane transport of a drug molecule
• Computational methods like quantitative structure-property relationship (QSPR) models are used to predict physicochemical properties based on the chemical structure of a compound

Permeability models

• Permeability models assess the ability of a drug to cross biological membranes, such as the intestinal epithelium or the blood-brain barrier
• In vitro permeability assays using cell-based models (Caco-2, MDCK) or artificial membranes (PAMPA) provide experimental data on drug permeability
• In silico models, such as structure-based or machine learning approaches, can predict permeability based on molecular descriptors and experimental data

Bioavailability estimation

• Bioavailability refers to the fraction of an administered dose that reaches the systemic circulation unchanged
• Estimation of bioavailability takes into account factors such as solubility, permeability, and first-pass metabolism
• In silico models, such as physiologically based pharmacokinetic (PBPK) models, can predict bioavailability by integrating absorption, distribution, metabolism, and excretion processes

Distribution prediction

• Distribution prediction focuses on estimating the extent and pattern of drug distribution throughout the body after absorption
• It involves evaluating the binding of a drug to plasma proteins, its volume of distribution, and its ability to penetrate specific tissues or barriers
• Understanding drug distribution is essential for determining the effective concentration at the target site and assessing potential off-target effects

Plasma protein binding

• Plasma protein binding refers to the reversible association of a drug with proteins in the blood, primarily albumin and alpha-1-acid glycoprotein (AAG)
• The extent of plasma protein binding affects the free drug concentration available for distribution to tissues and elimination from the body
• In vitro assays (equilibrium dialysis, ultrafiltration) and in silico models (QSAR, machine learning) are used to predict plasma protein binding based on drug structure and physicochemical properties

Volume of distribution

• Volume of distribution ($V_d$) is a theoretical volume that relates the amount of drug in the body to its concentration in the plasma at equilibrium
• It provides an estimate of the extent of drug distribution throughout the body and is influenced by factors such as tissue binding, blood flow, and physicochemical properties
• Physiologically based pharmacokinetic (PBPK) models and allometric scaling approaches are used to predict volume of distribution based on preclinical data and species extrapolation

Blood-brain barrier penetration

• The blood-brain barrier (BBB) is a selective barrier that regulates the entry of substances, including drugs, into the central nervous system (CNS)
• Predicting BBB penetration is crucial for CNS-targeted drugs to ensure adequate brain exposure and efficacy
• In vitro models (cell-based assays, PAMPA) and in silico approaches (QSAR, machine learning) are used to predict BBB permeability based on molecular properties and structural features

Metabolism prediction

• Metabolism prediction focuses on estimating the biotransformation of a drug by various enzymatic systems in the body
• It involves identifying the major metabolic pathways, predicting metabolite formation, and assessing the impact of metabolism on drug clearance and potential drug-drug interactions
• Accurate metabolism prediction is essential for optimizing drug exposure, minimizing toxicity, and understanding potential variability in drug response

Cytochrome P450 interactions

• Cytochrome P450 (CYP) enzymes are a family of heme-containing proteins responsible for the metabolism of many drugs and endogenous compounds
• Predicting CYP-mediated metabolism is crucial for assessing drug clearance, potential drug-drug interactions, and the formation of active or toxic metabolites
• In vitro assays (liver microsomes, hepatocytes) and in silico models (structure-based, ligand-based) are used to predict CYP substrate specificity, inhibition, and induction

Phase I vs Phase II reactions

• Drug metabolism is divided into Phase I and Phase II reactions
• Phase I reactions involve oxidation, reduction, or hydrolysis of the drug molecule, often catalyzed by CYP enzymes, and can result in the formation of active or inactive metabolites
• Phase II reactions involve conjugation of the drug or its metabolites with endogenous molecules (glucuronidation, sulfation, acetylation) to increase their polarity and facilitate excretion
• Predicting the balance between Phase I and Phase II reactions is important for understanding the overall metabolic fate of a drug

Metabolic stability assessment

• Metabolic stability refers to the resistance of a drug to biotransformation and is a key determinant of its half-life and clearance
• In vitro assays using liver microsomes or hepatocytes are commonly used to assess metabolic stability by measuring the rate of drug disappearance over time
• In silico models, such as quantitative structure-metabolism relationship (QSMR) and machine learning approaches, can predict metabolic stability based on molecular descriptors and experimental data

Excretion prediction

• Excretion prediction focuses on estimating the elimination of a drug and its metabolites from the body
• It involves evaluating the contributions of renal and biliary excretion to drug clearance and predicting the half-life of the drug
• Accurate excretion prediction is essential for determining the appropriate dosing regimen and understanding potential accumulation or toxicity risks

Renal clearance estimation

• Renal clearance refers to the elimination of a drug from the body through the kidneys and is a major route of excretion for many drugs
• It involves glomerular filtration, active secretion, and passive reabsorption processes in the nephron
• In vitro assays (kidney slices, cell lines) and in silico models (QSAR, PBPK) are used to predict renal clearance based on physicochemical properties and transporter interactions

Biliary excretion models

• Biliary excretion is the elimination of a drug or its metabolites from the body through the bile and feces
• It is mediated by transporters in the hepatocytes and can be a significant route of excretion for certain drugs, especially those with high molecular weight or polarity
• In vitro assays (sandwich-cultured hepatocytes, vesicle assays) and in silico models (QSAR, PBPK) are used to predict biliary excretion based on molecular properties and transporter interactions

Half-life prediction

• Half-life ($t_{1/2}$) is the time required for the concentration of a drug in the body to decrease by half and is a measure of its persistence in the system
• Predicting half-life is important for determining the dosing frequency and assessing the potential for accumulation or fluctuations in drug levels
• Half-life can be estimated from in vitro metabolic stability data using scaling factors or predicted using PBPK models that integrate absorption, distribution, metabolism, and excretion processes

Toxicity prediction

• Toxicity prediction focuses on estimating the potential adverse effects of a drug on various biological systems
• It involves evaluating the drug's ability to cause cellular damage, organ toxicity, or genotoxicity using in vitro assays and in silico models
• Early identification of potential toxicity issues is crucial for prioritizing compounds and minimizing the risk of failure in later stages of drug development

In vitro toxicity assays

• In vitro toxicity assays are cell-based or biochemical tests that assess the potential toxic effects of a drug on specific cellular processes or organelles
• Examples include cytotoxicity assays (MTT, LDH), mitochondrial toxicity assays (glucose/galactose assay), and high-content imaging assays (multi-parameter toxicity screening)
• These assays provide rapid and cost-effective screening of compounds for potential toxicity and help guide structure-activity relationship (SAR) studies

In silico toxicity models

• In silico toxicity models use computational methods to predict the potential toxicity of a drug based on its chemical structure and properties
• These models include quantitative structure-activity relationship (QSAR) models, rule-based expert systems, and machine learning approaches
• In silico models can predict various toxicity endpoints, such as mutagenicity, carcinogenicity, and organ-specific toxicity, by leveraging historical data and structural alerts

Genotoxicity vs organ toxicity

• Genotoxicity refers to the ability of a substance to cause damage to genetic material (DNA) and is a major concern in drug development due to its association with carcinogenicity
• Organ toxicity refers to the adverse effects of a drug on specific organs or tissues, such as hepatotoxicity, nephrotoxicity, or cardiotoxicity
• Predicting both genotoxicity and organ toxicity is essential for assessing the overall safety profile of a drug and guiding the selection of compounds with minimal toxicity risks

ADMET prediction methods

• ADMET prediction methods encompass a range of experimental and computational approaches used to estimate the pharmacokinetic and safety properties of drug candidates
• These methods include in vitro techniques, in silico models, and in vitro-in vivo extrapolation (IVIVE) strategies
• The choice of prediction methods depends on the specific ADMET property of interest, the stage of drug discovery, and the available resources and data

In vitro techniques

• In vitro techniques involve the use of cell-based assays, subcellular fractions, or artificial membrane systems to assess ADMET properties
• Examples include Caco-2 permeability assays for absorption, liver microsomal stability assays for metabolism, and cytotoxicity assays for toxicity
• In vitro techniques provide experimental data on specific ADMET endpoints and can be used to validate and refine in silico predictions

In silico approaches

• In silico approaches use computational methods to predict ADMET properties based on the chemical structure and properties of a drug molecule
• These approaches include quantitative structure-property relationship (QSPR) models, physiologically based pharmacokinetic (PBPK) models, and machine learning algorithms
• In silico models can rapidly screen large numbers of compounds and guide the prioritization of candidates for experimental testing

In vitro-in vivo extrapolation

• In vitro-in vivo extrapolation (IVIVE) is the process of translating in vitro ADMET data to in vivo predictions using mathematical models and scaling factors
• IVIVE allows for the prediction of pharmacokinetic parameters, such as clearance and bioavailability, based on in vitro data and physiological considerations
• IVIVE strategies, such as the well-stirred model and the parallel tube model, are used to integrate in vitro data with physiological parameters to generate in vivo predictions

Integration of ADMET data

• Integration of ADMET data involves combining information from multiple sources and endpoints to make informed decisions in drug discovery and development
• It requires consideration of the interplay between different ADMET properties and their impact on the overall pharmacokinetic and safety profile of a drug
• Effective integration of ADMET data enables the identification of compounds with optimal pharmacokinetic and safety characteristics and guides the optimization of lead compounds

Multi-parameter optimization

• Multi-parameter optimization (MPO) is a strategy that involves simultaneously optimizing multiple ADMET properties to achieve a balanced pharmacokinetic and safety profile
• MPO considers the trade-offs between different ADMET endpoints and assigns weights to each property based on its relative importance
• Computational tools, such as desirability functions and multi-objective optimization algorithms, are used to identify compounds with the most favorable combination of ADMET properties

ADMET property trade-offs

• ADMET properties often exhibit trade-offs, where the improvement of one property may adversely affect another
• For example, increasing lipophilicity to improve permeability may also increase the risk of metabolic instability and toxicity
• Understanding and balancing these trade-offs is crucial for optimizing the overall pharmacokinetic and safety profile of a drug

Decision-making in drug design

• Integration of ADMET data supports decision-making in drug design by providing a comprehensive view of the pharmacokinetic and safety characteristics of a compound
• ADMET data can be used to prioritize compounds for further development, guide structural modifications to address specific liabilities, and inform the selection of appropriate preclinical models
• Effective decision-making based on ADMET data requires collaboration between medicinal chemists, DMPK scientists, and toxicologists to optimize compounds and mitigate potential risks

Challenges in ADMET prediction

• ADMET prediction faces several challenges that can impact the accuracy and reliability of the predictions
• These challenges include species differences, inter-individual variability, and limitations in the predictive power of current models
• Addressing these challenges requires continuous refinement of prediction methods, integration of diverse data sources, and a critical evaluation of the limitations and uncertainties associated with ADMET predictions

Species differences

• ADMET properties can vary significantly between different species, making extrapolation from animal models to humans challenging
• Differences in physiology, metabolism, and transporter expression can lead to discrepancies in pharmacokinetic and toxicity profiles between species
• Strategies such as allometric scaling and PBPK modeling are used to account for species differences and improve the translation of preclinical data to human predictions

Inter-individual variability

• Inter-individual variability in ADMET properties arises from genetic, demographic, and environmental factors that influence drug disposition and response
• Polymorphisms in drug-metabolizing enzymes (CYPs) and transporters can lead to significant differences in drug exposure and toxicity risk among individuals
• Incorporating population variability into ADMET predictions, through the use of virtual populations and sensitivity analyses, can help assess the potential impact of inter-individual differences on drug performance

Prediction accuracy limitations

• The accuracy of ADMET predictions is limited by the quality and quantity of available data, the complexity of biological systems, and the inherent uncertainties in computational models
• In silico models are based on historical data and may not capture novel chemical spaces or mechanisms of action
• Continuous validation and refinement of prediction models, along with the integration of diverse data sources (in vitro, in vivo, clinical), are necessary to improve the accuracy and applicability of ADMET predictions

Future perspectives

• The field of ADMET prediction is continuously evolving, driven by advances in experimental techniques, computational methods, and our understanding of biological systems
• Future perspectives in ADMET prediction include the development of emerging technologies, refinement of prediction models, and integration with systems biology approaches
• These advancements aim to improve the accuracy, efficiency, and translational value of ADMET predictions in drug discovery and development

Emerging technologies

• Emerging technologies, such as organ-on-a-chip systems and 3D cell culture models, provide more physiologically relevant platforms for ADMET assessment
• These technologies enable the study of drug disposition and toxicity in complex, multi-cellular environments that better mimic in vivo conditions
• Integration of these advanced in vitro models with computational approaches can enhance the predictive power of ADMET models and reduce the reliance on animal studies

Refinement of prediction models

• Refinement of prediction models involves the incorporation of new data sources, advanced computational methods, and a deeper understanding of the underlying biological mechanisms
• Machine learning and artificial intelligence approaches, such as deep learning and multi-task learning, can leverage large datasets to improve the accuracy and generalizability of ADMET predictions
• Integration of multi-omics data (genomics, proteomics, metabolomics) into prediction models can provide a more comprehensive view of the factors influencing drug disposition and response

Integration with systems biology

• Integration of ADMET prediction with systems biology approaches enables a holistic understanding of drug behavior in the context of complex biological networks
• Systems pharmacology models, which incorporate ADMET properties along with target engagement and downstream signaling pathways, can predict the efficacy and safety of drugs at a systems level
• Integration of ADMET predictions