Screening and PKPD modeling Flashcards
How experimental PK/PD in vitro data can be translated into clinical decision-making.
Understanding PK/PD Relationships: PK deals with the absorption, distribution, metabolism, and excretion (ADME) of a drug in the body, while PD deals with the drug’s effect on the body (e.g., efficacy, toxicity). In vitro studies provide data on drug concentrations and their effects in controlled laboratory settings.
Modeling and Simulation: PK/PD modeling involves mathematical modeling to describe the relationship between drug concentration and its effect. In vitro data can be used to develop these models, which can then be extrapolated to predict PK/PD behavior in vivo.
Translational Research: Bridging the gap between in vitro and in vivo data involves translational research. This includes understanding the physiological differences between laboratory models and humans, such as metabolism, organ function, and drug interactions.
Clinical Trials Design: PK/PD data can inform the design of clinical trials, including dosing regimens, patient selection, and endpoints. For example, PK data can inform dose selection to achieve therapeutic concentrations, while PD data can help determine the optimal endpoint for assessing drug efficacy or toxicity.
Dose Optimization: Using PK/PD data, clinicians can optimize drug dosing regimens based on individual patient characteristics such as age, weight, renal function, and co-morbidities. This personalized approach can maximize therapeutic efficacy while minimizing the risk of adverse effects.
Therapeutic Drug Monitoring (TDM): TDM involves measuring drug concentrations in patients’ blood or tissues to optimize dosing regimens. PK/PD data can guide TDM protocols by defining target drug concentrations associated with optimal therapeutic outcomes.
Risk Assessment: PK/PD data can help assess the risk of adverse drug reactions or drug interactions, guiding clinicians in monitoring and managing patient safety.
Regulatory Decision-Making: Regulatory agencies may use PK/PD data to evaluate the safety and efficacy of new drugs, determine appropriate dosing recommendations, and approve labeling information for clinical use.
Clinical Guidelines: Professional medical societies may incorporate PK/PD data into clinical practice guidelines to assist clinicians in making evidence-based treatment decisions.
Continual Evaluation: As new clinical data becomes available, PK/PD models and clinical decision-making algorithms should be continually evaluated and refined to ensure optimal patient outcomes.
How selected physiological parameters can impact PK/PD.
Renal Function: Renal function affects drug clearance, particularly for drugs that are predominantly eliminated through the kidneys. Impaired renal function can lead to decreased drug clearance, resulting in higher drug concentrations and increased risk of toxicity.
Hepatic Function: Hepatic function influences drug metabolism, primarily through the cytochrome P450 enzyme system. Impaired liver function can decrease drug metabolism, prolonging drug half-life and potentially increasing drug toxicity. Conversely, induction of hepatic enzymes can accelerate drug metabolism, reducing drug efficacy.
Body Weight and Composition: Body weight and composition can impact drug distribution and clearance. For lipophilic drugs, which tend to distribute into fatty tissues, higher body fat content can lead to prolonged drug action. Conversely, for hydrophilic drugs, which distribute primarily into lean body mass, variations in body composition can affect drug distribution volume and clearance rates.
Age: Age-related changes in physiology can affect both PK and PD parameters. For example, decreased hepatic and renal function in elderly patients can alter drug metabolism and elimination, leading to increased drug exposure and risk of toxicity. Additionally, age-related changes in receptor sensitivity or organ function may affect drug response and efficacy.
Genetic Variability: Genetic polymorphisms in drug-metabolizing enzymes, transporters, and drug targets can influence individual variability in drug response. Pharmacogenetic factors can affect drug metabolism, efficacy, and toxicity, leading to inter-individual differences in PK/PD profiles.
Drug-Drug Interactions: Concurrent use of multiple drugs can alter PK/PD parameters through drug-drug interactions. Interactions may involve inhibition or induction of drug-metabolizing enzymes or transporters, leading to changes in drug absorption, distribution, metabolism, or elimination.
Disease States: Underlying medical conditions can impact PK/PD parameters through alterations in organ function, blood flow, protein binding, or receptor sensitivity. Disease-related changes in physiology may affect drug absorption, distribution, metabolism, or elimination, leading to variability in drug response and efficacy.
Know which factors are important for the planning of high-throughput drug screening.
Choice of model system,
choice of Data collection methods,
What to measure
Assay Development:
Design and optimize robust assays that accurately reflect the biological processes of interest.
Ensure the assays are scalable and amenable to automation.
Target Selection:
Clearly define the molecular target or pathway being investigated.
Prioritize targets based on relevance to the disease of interest and potential for therapeutic intervention.
Compound Libraries:
Curate diverse and representative compound libraries for screening.
Consider including known bioactives, FDA-approved drugs, and structurally diverse compounds.
Automation and Robotics:
Implement automated systems to handle liquid handling, plate reading, and other repetitive tasks.
Utilize robotics to increase throughput and reduce human error.
Data Management:
Establish a robust system for data acquisition, storage, and analysis.
Implement informatics tools to handle large datasets efficiently.
Quality Control and Validation:
Develop quality control measures to ensure the reliability of screening results.
Validate screening assays to ensure reproducibility and consistency.
Dose-Response Analysis:
Perform dose-response analysis for hit confirmation and determination of compound potency.
Assess the concentration-response relationship for identified compounds.
Hit Triage and Confirmation:
Develop criteria for hit selection based on assay performance and compound activity.
Confirm hits using orthogonal assays or additional experiments to eliminate false positives.
Chemoinformatics and Computational Approaches:
Utilize computational methods for virtual screening and prioritization of compounds.
Employ chemoinformatics tools to analyze chemical structures and predict bioactivity.
Ethical and Regulatory Considerations:
Adhere to ethical standards and regulatory guidelines in the handling and testing of compounds.
Consider safety, toxicity, and other relevant factors when selecting compounds for screening.
Know which factors are important for the implementation of high-throughput drug screening.
Assay Robustness and Reproducibility:
Develop assays that are robust, reproducible, and amenable to automation.
Optimize assay conditions to minimize variability and ensure consistent results across multiple screenings.
Automation and Robotics:
Utilize automated liquid handling systems, robotics, and other high-throughput technologies to increase screening efficiency.
Implement robotics for tasks such as compound dispensing, plate handling, and data acquisition to reduce human error and increase throughput.
Assay Miniaturization:
Miniaturize assays to reduce reagent and compound consumption, enabling cost-effective high-throughput screening.
Consider using microplates with smaller well volumes to achieve higher throughput without compromising assay quality.
Compound Libraries:
Curate diverse and representative compound libraries for screening.
Include known bioactives, FDA-approved drugs, and structurally diverse compounds in the library for a comprehensive screening approach.
Quality Control Standards:
Implement rigorous quality control standards to ensure the reliability of screening results.
Include positive and negative controls to monitor assay performance and detect potential issues.
Data Management and Informatics:
Establish a robust data management system to handle large datasets generated during screening.
Implement informatics tools for data analysis, visualization, and interpretation to extract meaningful insights.
High-Throughput Screening Platforms:
Choose appropriate screening platforms, such as fluorescence, luminescence, or absorbance-based methods, based on the assay requirements.
Consider multiplexing and parallelization to increase throughput.
Hit Triage and Confirmation:
Develop criteria for hit selection based on assay performance and compound activity.
Confirm hits using orthogonal assays or additional experiments to eliminate false positives and ensure the validity of screening results.
Hit-to-Lead Optimization:
Establish a process for hit-to-lead optimization to prioritize and refine potential drug candidates.
Know which factors are important for the interpretation of high-throughput drug screening.
Assay Validation and Quality Control:
Ensure that the screening assay is well-validated and meets predefined quality control standards.
Perform regular quality control checks to monitor assay performance and identify any issues that may affect data interpretation.
Reproducibility and Robustness:
Assess the reproducibility and robustness of the screening assay. Consistent results across replicate experiments increase confidence in the identified hits.
Address any sources of variability that may impact the reliability of the screening data.
Normalization and Data Transformation:
Normalize raw data to correct for experimental variability, plate effects, and other sources of bias.
Consider data transformation techniques to ensure that the data meet the assumptions of the statistical methods used for analysis.
Hit Selection Criteria:
Define clear and stringent criteria for hit selection based on assay performance and compound activity.
Consider using statistical methods to identify hits that significantly deviate from the baseline.
Dose-Response Analysis:
Perform dose-response analysis for hit confirmation and determination of compound potency.
Evaluate concentration-response relationships to assess the effectiveness and concentration-dependent effects of identified hits.
False Positive and False Negative Analysis:
Investigate potential sources of false positives and false negatives in the screening data.
Use orthogonal assays or validation experiments to confirm hits and eliminate false positives.
Chemical Structure and Mechanism of Action:
Analyze the chemical structures of hits to identify potential scaffolds and chemical series.
Consider the mechanism of action of identified compounds and their relevance to the target or pathway of interest.
Data Integration:
Integrate HTS data with other relevant datasets, such as genomics, transcriptomics, or proteomics, to gain a more comprehensive understanding of compound effects.
Utilize systems biology approaches for a holistic interpretation of drug responses.
Biological Relevance:
Evaluate the biological relevance of identified hits in the context of the disease or biological process being studied.
Consider whether the observed effects are physiologically relevant and biologically meaningful.
Hit-to-Lead Optimization:
Prioritize hits based on their potential for further development.
Consider additional optimization steps, such as medicinal chemistry efforts, to improve compound properties and increase their potential as drug candidates.
Identification of Off-Target Effects:
Assess the potential for off-target effects of identified hits.
Utilize computational tools and databases to predict off-target interactions and evaluate their impact on the interpretation of screening results.
Data Visualization and Reporting:
Use effective data visualization tools to present screening results clearly.
Prepare comprehensive reports that include key findings, statistical analyses, and interpretations for easy communication with stakeholders.
Validation in Relevant Models:
Validate the identified hits in relevant in vivo models or primary patient samples to confirm their efficacy and safety.
Consider the translational potential of hits for further preclinical and clinical development.
Funnel strategy in drug screening
Primary assay
= assay used for the screening campaign,
Need to be miniaturized, have excellent throughput
and low cost per data point
Counter assay
= to filter out false positive (technology hitters) or
unselective compounds
Sometimes just like the primary assay but with one key
modification.
dose deteremination
Secondary/orthogonal assays
= assays that look at the same thing than the primary assay
but with a different technology or assay that look at other thing
for instance biological effect
Can be more focused with lower throughput
Hit selection
the clinical trial timeline
Phase II & Phase III
Dose, Efficacy, Toxicity
Phase I
PK, Dose escalation, Toxicity
Pre-clinical test
& Lead optimization
SAR, Drug-like properties, Solubility
Permeability, ADME, Plasma PK
Efficacy, Toxicity
Compound screening
Visual screening, HTS
Target validation
Disease models, Target identification, Target validation
why is there a need for The need for HTS?
- Financial pressures due to the increasing cost of bringing a drug to market
- The ‘post-genome era’ à increase in the number of targets of therapeutic interest that
have unknown small-molecule modulators. - Stringent safety requirements by regulatory authorities à substantial stress on the
research and development functions in pharmaceutical and biotechnology companies - Generating new molecular entities against 3 to 4 new targets per year
why is HTS important for drug discovery?
- High-throughput screening for novel drug discovery to substitute the traditional “trial
and error” approach - To identify therapeutic targets and validate biological effects
- HTS involves assaying and screening a large number of biological effectors and
modulators against designated and exclusive targets - HTS is generally favored when little is known of the target,
- HTS can be paired with other strategies such as computational techniques and fragmentbased
drug design
HTS comprises several steps:
- Target recognition,
- Compound management
- Reagent preparation
- Assay development
- The screening itself.
HTS platforms ( types of assay) , how we chose them ?
–HTS involves in vitro (cell, Biochemical ) or wholeorganism- based assays
* The most common readouts for
biochemical assays in HTS are optical,
including absorbance, fluorescence,
luminescence, and scintillation.
* The efficiency of data production and
cost per screen are the main
determinants in the choice of the most
suitable readout for a particular screen.
what is the main reason drugs fail in clinical trials or are withdrawn ?
toxicity issues
Animal models are poor predictors of drug safety in humans ( example )
- Fialuridine anti HBV
- 5 volunteers died during phase II clinical trials
- Safe in mice, rats, dogs, monkeys, and woodchucks (doses»_space;»)
- 2 volunteers survived after receiving liver transplants
Transporter protein expressed in mitochondria in human hepatocytes à mitochondrial toxicity
3D cell culture models in drug discovery
- Promising model for studying disease mechanisms
- Application in drug discovery
- Potentially recapitulate aspects of in vivo physiology more accurately than 2D systems
- 3D vs 2D cultures differ in many aspects (proliferation, differentiation, migration,
oxygen and nutrient gradients, cell-cell and cell-extracellular matrix interactions) - àimpact cellular phenotypes and drug responses à high failure rates in early stages of
phenotypic drug screens if performed only in 2D cultures
Primary Human Hepatocyte (PHH) model preserves hepatic molecular
signature
- The PHH model maintains
physiological phenotype at
transcriptomic and proteomic level
compared to 2D culture - Cells in 2D lost the in vivo
signature after 24hrs
A good cellular model for HTS should:
- Recapitulate the physiological and functional characteristics of the organ to be
studied - Show the disease hallmarks
- Scalable system compatible with HTS
- In vivo models have poor outcome in drug screening and are not compatible
with HTS - 3D cellular models are a suitable alternative for HTS compared to 2D
What are the challenges and considerations when using patient-derived material
in drug screening?
Heterogeneity of Patient Samples:
Patient samples can exhibit significant biological variability due to differences in genetic backgrounds, disease subtypes, and individual responses to treatment.
Addressing this heterogeneity is essential for obtaining meaningful and reproducible results.
Sample Availability and Collection:
Obtaining an adequate supply of patient-derived material can be challenging, especially for rare diseases or situations where obtaining samples involves invasive procedures.
Proper consent and ethical considerations must be addressed in the collection of patient samples.
Sample Processing and Storage:
Standardized protocols for sample processing and storage are crucial to maintaining sample integrity.
Variability in sample handling can impact assay results, necessitating careful documentation of sample processing steps.
Informed Consent and Ethical Considerations:
Ensure proper informed consent from patients for the use of their biological material in research.
Comply with ethical guidelines and regulations related to human subject research.
Limited Availability of Matched Healthy Controls:
Obtaining appropriate control samples that match the characteristics of patient samples can be challenging.
The lack of well-matched controls may complicate the interpretation of screening results.
Technical Challenges in Assay Development:
Patient-derived samples may present technical challenges due to variations in cell viability, growth rates, and other biological parameters.
Assay development may require optimization to accommodate the specific characteristics of patient samples.
Genetic and Molecular Heterogeneity:
Diseases often exhibit genetic and molecular heterogeneity, which can complicate drug screening efforts.
Molecular profiling of patient samples may be necessary to stratify them into subgroups for more targeted screening.
Disease Progression and Evolution:
Patient-derived samples may represent a snapshot of the disease at a specific point in time.
Longitudinal studies or the use of multiple samples from the same patient over time may be needed to capture disease progression and evolution.
Reproducibility and Standardization:
Achieving reproducibility across experiments and laboratories can be challenging when using patient-derived material.
Standardizing protocols and ensuring rigorous quality control measures are critical.
Data Interpretation and Translation to the Clinic:
Translating findings from patient-derived material to clinical applications requires careful consideration of the relevance and predictive value of screening results.
Addressing the gap between preclinical findings and clinical efficacy is a complex process
Understand the different techniques/applications of 3D cell culture in drug
screening
Spheroid Culture:
Technique: Cells are allowed to aggregate and form three-dimensional spheroids.
Applications: Mimics tissue architecture and promotes cell-cell interactions. Suitable for assessing drug penetration, migration, invasion, and responses in a more physiological context.
Organoids:
Technique: Culturing cells derived from tissues or organs to form complex, three-dimensional structures.
Applications: Replicates tissue architecture, cellular heterogeneity, and organ-specific functions. Used for drug testing, disease modeling, and personalized medicine.
Bioprinting:
Technique: 3D printing of cell-laden biomaterials to create tissue-like structures.
Applications: Enables the precise placement of cells in a 3D spatial arrangement. Useful for constructing tissue models for drug testing and regenerative medicine.
Hydrogel-Based 3D Culture:
Technique: Cells are encapsulated within hydrogel matrices to create a 3D environment.
Applications: Provides a tunable and customizable platform for studying cell behavior, drug responses, and interactions within a biomimetic microenvironment.
Microfluidic Systems:
Technique: Cells are cultured in microscale channels or chambers, allowing for precise control over the cellular microenvironment.
Applications: Facilitates high-throughput screening and real-time monitoring of cellular responses. Enables the study of cell migration, drug diffusion, and cell-cell interactions.
Hanging Drop Method:
Technique: Cells are suspended as small droplets, promoting self-aggregation.
Applications: Suitable for spheroid formation and high-throughput drug screening. Mimics cell-cell interactions and cell morphology found in vivo.
Rotary Cell Culture Systems:
Technique: Cells are cultured in rotating vessels to simulate microgravity conditions.
Applications: Useful for studying the effects of altered gravity on cell behavior. Can be employed in drug screening for space-related research.
Magnetic Levitation:
Technique: Cells are combined with magnetic nanoparticles and levitated in a magnetic field.
Applications: Mimics the cellular microenvironment and allows for the study of cell-cell interactions, drug responses, and migration in a 3D context.
Coculture Systems:
Technique: Different cell types are cultured together to model complex tissue interactions.
Applications: Enables the study of cell communication, paracrine signaling, and interactions between different cell types. Useful for drug screening in a multicellular context.
Tumor-on-a-Chip Models:
Technique: Integrates microfabrication techniques with 3D cell culture to create microscale models of tumors.
Applications: Mimics the tumor microenvironment, vasculature, and drug responses. Useful for studying cancer biology and drug screening.
Extracellular Matrix (ECM) Incorporation:
Technique: Incorporates natural or synthetic ECM components into 3D cultures.
Applications: Enhances cell-matrix interactions, providing a more physiologically relevant environment for drug screening.
What are the advantages of using 3D cell culture models in drug screening
Mimicking In Vivo Microenvironment:
3D cell cultures better replicate the complex cellular microenvironment found in living tissues compared to 2D cultures. This includes cell-cell interactions, gradients of nutrients and oxygen, and extracellular matrix (ECM) composition.
Cell-Cell Interactions:
3D models allow for the formation of cell-cell interactions, which are crucial for simulating tissue architecture and function. This is particularly important in understanding complex biological processes and responses to drugs.
Cell-ECM Interactions:
The inclusion of ECM components in 3D cultures enables cells to interact with their surrounding matrix, influencing cell behavior, migration, and signaling pathways. This more accurately reflects in vivo conditions.
Physiological Relevance:
3D cultures provide a more physiologically relevant environment, leading to improved cell differentiation, phenotype maintenance, and overall cellular function. This is critical for studying drug responses in a context that better represents in vivo conditions.
Cellular Heterogeneity:
3D models allow for the maintenance of cellular heterogeneity, preserving the diversity of cell types and their interactions within tissues. This is particularly relevant in diseases where multiple cell types contribute to pathology.
Drug Penetration and Distribution:
3D cultures better mimic drug penetration and distribution within tissues, providing insights into how drugs interact with cells in a 3D spatial arrangement. This is crucial for understanding drug efficacy and toxicity.
Disease Modeling:
3D cell culture models can be tailored to mimic specific disease conditions, allowing for the study of disease progression, pathophysiology, and drug responses. This is particularly valuable for diseases with complex and multifactorial etiologies.
High-Throughput Screening:
Advances in automation and imaging technologies have made it feasible to perform high-throughput screening using 3D cell cultures. This enables the screening of large compound libraries in a more physiologically relevant context.
Prediction of In Vivo Responses:
3D models provide a better correlation with in vivo responses compared to 2D models, improving the predictive value of preclinical studies. This can lead to more accurate assessments of drug safety and efficacy.
Personalized Medicine:
Patient-derived 3D cultures can be used to create personalized models for drug screening, considering individual genetic and molecular characteristics. This approach holds promise for tailoring treatments based on patient-specific responses.
Reduction of Animal Testing:
The use of 3D cell cultures can contribute to the reduction of animal testing by providing more relevant and predictive preclinical models. This aligns with ethical considerations and regulatory trends.
Understanding Tumor Biology:
3D models, such as spheroids and organoids, are valuable for studying tumor biology, including aspects of tumor growth, invasion, and metastasis. These models are particularly relevant in cancer drug discovery.
why is there a Translational problem in drug development ?
- Despite outstanding technological achievement and
accumulated knowledge high attrition rates and failure in
drug development. - Drug development is a complex problem where we try to predict
drug behavior in humans based on other systems. - Knowledge of human physiology and pharmacology is
fragmented. - Need for more basic and translational research and better
integration of findings from different scientific disciplines
Need for in-silico PKPD studies ?
- PKPD studies are the core of the drug discovery and
development process - Optimising PKPD properties for human disease is the ultimate goal of
the drug discovery/development journey - Need to establish a quantitative link between experimental
PK/PD data and our understanding of the biological
mechanisms that generated the data
We need a MODEL (PKPD model)
What is a model? - definition
- Model: a simplified representation of a real-world system or
process - A box of relationships that describes a system using the system
parameters. - How do you think your data were generated?
- Some models
- Newton’s second law of motion: F = ma
- Special relativity: E = mc2
- Fick law :
Modeling and simulation
Modeling
Confirms
knowledge
SEARCH for optimal parameter
values that explains the data
F = 0.5, V = 20L, CL = 2 L/h
Explore
possibilites
Parameters are FIXED to
F = 0.5, V = 20L, CL = 2 L/h
do absorption distribution and elimination happen simultaneusly ?
yes
Pharmacokinetic parameters - Human
- Clearance
- Volume of distribution
- Half-life
- Bioavailability
Clearance (CL)
- It is the rate at which a drug is eliminated from the body = the
volume of blood cleared of the drug per unit of time. - It is the irreversible removal of a drug from the measured matrix
(blood or plasma) permanent change. - CL unit: volume/time
- CL is typically constant
CL = Rate of elimination constant(ke) * Vd Vd = Dose/C0
crearance is different for oral versus IV administration
Clearance (CL) – Organ CL
* CL can be measured for each organ
CLsystemic = CLrenal + CLhepatic + CLother
and for one organ its Q*E with :
E=(Cin-Cout)/Cin
what is Volume of distribution (V)?
It is a theoretical concept: The volume in which the amount of
drug in the body would be needed to uniformly distribute and
produce the observed concentration.
* Relates the drug concentration in plasma to the total amount of
drug in the body
or
Reversible partitioning of drug molecules into various tissues from plasma
V =Total amount of drug (A) (mass)/Plasma concentration (C) (mass/volume)
- At time = 0
- Drug amount in the body = dose
- Concentration = C0
Volume of distribution (V)
* Small values of V Drug distributes in the central compartment
* Large values of V Central compartment + other
* The volume of distribution can be larger than the plasma volume
(approx. 3L) or the whole body volume.
Half-life
- It is the time for a drug to be reduced by 50%
ln2/ke or 0,693 *V/CL - Determine when reaching the steady state (Css)
- The Css is reached after 4-5 half-lives
- Determine the time required to eliminate all or a
portion of the drug if the treatment is discontinued - Determine dosing frequency
- Longer half-life longer dosing interval
Bioavailability (F)
- Percentage or fraction of administrated dose
that reaches the systemic circulation: F - The IV bolus is the reference
F= AUC oral / AUC IV - Amount of absorbed drug = F * Dose
Prediction of oral absorption
Bioavailability (F)
(The extent of absorption)
The absorption rate constant (ka)
(The speed of absorption)
Lag time (only humans)
(The delay in absorption)
Determination of Fa
- Earlier methods = Ex-vivo
1. Animal intestine
2. Endoscopic balloons
Gives remaining/absorbed drug
concentration in the intestine.
Prediction of Fa - Caco2 permeability
- Fa is determined using the Caco2 cell line
- Derived from a colon carcinoma
- Ability to differentiate into a monolayer of
cells - Has the enterocytes absorption properties
with a brush border layer typically found in
small intestines. - Papp: apparent permeability (Caco2 assay)
- Peff: effective permeability (Human)
- Tres: transit time in the human small intestine (3h)
- R: radius of the human small intestine
- Papp and Peff are closely correlated
Prediction of Fa - Challenges
* Caco2 express P-gp and MRP2
* Passive drug permeability estimation is
affected when the drug is a substrate of P-gp
MDCK (Madin-Darby Canine Kidney) cells
* High Papp Inter-laboratory variability
especially when Peff < 1x10-4 cm/s
Prediction of Fg
- No consensus on how to estimate Fg
- Yang et al model
Extrapolate from hepatic CL
Prediction of Fg
* Can be estimated from in-vitro assays
* Simplified form: Total fraction of the drug
* Can take into account the unbound (free) fraction of the drug
What is Fa , Fg , Fh
Membrane
penetration (Fa)
Metabolism in the
enterocyte (Fg)
Metabolism
during first pass
through liver (Fh)
Fh ?
Fh = 1 - Eh
(CL= Qh* Eh)
Eh = (CLin - CLout) / CLin
Q: Blood flow
E: Extraction coefficient
Bioavailability
F = 0.8 * 0.9 *0.7 = 0.5 = 50%
Effective dose = Administrated Dose * F = 100 mg * 0.5 = 50mg
Prediction of ka
- In-vitro assays
- Papp : drug permeability across intestinal mucosa
(Caco-2) - S: absorptive surface area (200 m2)
- Vc: central volume of distribution
- ka can be estimated using:
- Assays (Caco-2)
- Allometric scaling from other species
- Averaging of ka from different species
- In-silico methods
- It is also possible to predict ka from the physicochemical
property of drugs, pH, CYP activity, transporter, and blood flow
(e.g., GastroPlus)
ka=Papp *S/ Vc
CL prediction
Methods
Hepatic CL:
* In vitro estimation
* Metabolite formation rate
* Substrate depletion
* Allometry can also be used
Renal CL:
* Allometry recommended for renal CL : glomerular filtration is well correlated
with the body size (Body weight of Body surface area: W/L^2) simple to scale up
to human.
Prediction of the CLh - The unbound fraction (CLint,u)
- Highly bound drugs
- low unbound fraction (fu)
- low exposure free drug
- low CLint,app
- CLint,app needs to be corrected for the unbound
fraction
CLint,u=CLint,app /fu
