Pharmacokinetics PDF

PHARMACOKINETICS By: Rida Jamil O V E RV I E W O F P H A R M A C O K I N E T I C S  Pharmacokinetics  is the study of drugs in the body – ‘what the body does to the drug’, it involved measuring and interpreting changes in the drug/metabolite concentrations in plasma, urine and oth...

PHARMACOKINETICS By: Rida Jamil O V E RV I E W O F P H A R M A C O K I N E T I C S  Pharmacokinetics  is the study of drugs in the body – ‘what the body does to the drug’, it involved measuring and interpreting changes in the drug/metabolite concentrations in plasma, urine and other body regions over time  Provides understanding of drug distribution, movement and effects in the body  Relies on ADME  Absorption, Distribution, Metabolism and Excretion  Pharmacodynamics  Describes the effects of the drugs on the body – ‘what the drug does to the body’ and focuses on drug interactions with receptors or other molecular targets W H Y I S P K I M P O RTA N T I N P H A R M A C O L O GY  PK development  feasible in the 20th century with advanced analytical techniques like high performance chromatography and mass spectrometry  In silico computer modelling PK has gained its importance  Practical focus: Primarily measures drug concentrations in the blood plasma due to its relation to extracellular fluid concentrations and supports target concentration strategy, linking to the biological effect to plasma drug concentration rather than dose  Individual variability: Plasma concentration (Cp) helps account for variations in drug absorption, distribution and elimination among individuals  Therapeutic Drug Monitoring (TDM): Used for drugs with a narrow therapeutic range to individualize dosage and minimise side-effects  It involves sequential blood samples and dose adjustments which can be costly  Preference for drugs with a large margin of safety that do not require intensive monitoring  C O N T I N U AT I O N …  Clinical practice changes: Direct oral anticoagulants have replaced warfarin due to ease of use and reduced need of monitoring  Future improvements aim to reduce the need for monitoring plasma concentrations for more drugs  Formal interpretation of PK data: involves a concentration vs. time data model (abstract or physiologically- based)  Dose adjustment is a parameter used to adjust dose regimens to achieve desired plasma concentrations.  Estimation of active concentration range: based on preclinical experiments on cells, tissues, or laboratory animals. Modified with data from early-phase human pharmacology trials  Early-Phase Human Trials: Begins with a single ascending dose (SAD) studies and successive groups of volunteers receive progressively increasing doses  Descriptive PK parameters: Estimated inspecting the time course of drug concentration in plasma Examples: Maximum plasma concentration (C_max) & Time to achieve C_max (T_max) Mathematical estimated parameters: Volume of distribution (Vd) and Clearance (CL)  A P P L I C AT I O N T O D I F F E R E N T D R U G S A N D I M P O RTA N T C O N S I D E R AT I O N S …  Application to different drug types: applicable to LMW drugs and macromolecular biopharmaceuticals.  PK parameters differ significantly between these types  Therapeutic antibodies typically have low clearance rates and long elimination half-lives  Important considerations:  Dose-related adverse effects often occur around C_max  Qualitative aspects of absorption, distribution and elimination vary between drug types  USES OF PK  Importance of PK in drug development: Crucial for interpreting toxicological and pharmacological data  Helps determine appropriate dose and dosing regimens for clinical trials  Dosing in animal studies: They often need to be much greater than in humans due to faster drug metabolism in animals like rodents and Methadone is an example where this difference is significant  Allometric scaling: Animal data used to estimate human equivalent doses via normalization to body SA rather than body weight & Paediatricians use this approach to estimate doses for children from adult doses  Early-Phase human trials: Dose escalation informed by real-time drug exposure data to ensure safety and Key parameters: C_max (peak conc. Related events) and AUC (cumulative toxicity)  Regulatory concepts: Bioavailability and bioequivalence support licensing of generic drugs and biosimilars concept for biopharmaceuticals, which undergo post-translational modifications  Biosimilars: monoclonal antibodies, soluble receptors, growth factors and hormones, these are biotherapeutic products with similar efficacy and quality to licensed bio-originator  PK principles in clinical practice: Understanding dosing regimens, timing blood sampling and interpreting drug concentrations for TDM.  Adjusting doses rationally and identifying drug interactions  Critical for intensive care specialists and anaesthetists managing severely ill patients  Individualizing dose regimens based on urgency of therapeutic needs and potential PK changes due to illness (e.g. renal or liver impairment)  IN THIS POWERPOINT, I WILL COVER…  Total drug clearance and steady state plasma concentrations:  Steady state plasma concentrations during intravenous infusion or repeated dosing is determined by total drug clearance  Higher clearance results in lower steady-state concentration for a given dosing rate  Single compartment model: Represents the body as a single-well stirred concentration with volume (Vd), predicts drug concentration over time, describing: Accumulation before reaching steady-state Decline of concentration after discontinuing dosing Limitations of single compartment model Inadequate for drugs with complex distribution and introduces the need for a 2-compartment model Two compartment model: More accurately represents drugs with distinct distribution phases (central and peripheral compartments) Non-linear kinetics: Occurs when drug clearance varies with concentration and requires different modelling approaches compared to linear kinetics Population kinetics: Used in situations with limited samples per subject, such as paediatrics and analyses data from a population to predict drug concentration and optimize dosing  D R U G E L I M I N AT I O N E X P R E S S E D A S C L E A RA N C E  Total Drug Clearance (CL-tot)  Fundamental PK parameter for elimination and defined as volume of plasma cleared of the drug per unit of time (e.g. mL/min or L/h)  Sum of renal clearance (CL-ren), metabolic clearance (CL-met) and other routes of elimination (e.g. faeces and breath)  Rate of drug elimination: Described by the equation  Rate of drug elimination = Cp x CL-tot  Can be measured during a constant-rate intravenous infusion until steady state is reached  At steady state: Rate of input = rate of elimination  Calculation of CL-tot: CL-tot = X/C_ss (X= infusion rate and C_ss = steady state plasma concentration), this is independent of any compartment model  Linear Kinetics: Many drugs clearance is dose-independent within the therapeutic range, doubling the infusion rate doubles the steady-state plasma concentration and plot of infusion rate (X) vs. C_ss is a straight line with a slope of Cl_tot  Single intravenous bolus dose: Cl_tot can be estimated by measuring plasma concentrations over time  AUC_0 is calculated to provide measures of clearance and drug exposure  Graphical estimation of AUC using the trapezoidal rule  CL_tot from AUC: CL_tot = Q/AUC (Q = bolus dose), AUC obtained from slope of the exponential decline in plasma concentration and elimination rate constant (K_el) used in calculations: AUC_0 = C_0/k_el (C_0 = initial plasma concentration)  Units and practical considerations: AUC has units of time x concentration and CL_tot has units of volume/time  S I N G L E C O M PA RT M E N T M O D E L  Represents the body as a single-well-stirred compartment with volume distributed (Vd), drug quantity Q introduced rapidly by intravenous injection and removed by metabolism or excretion  Initial concentration (C_0)  C_0 = Q/ Vd & estimated by extrapolating a semilogarithmic plot of plasma concentration (C_p) vs. time back to 0.  Linear kinetics and exponential decay: Many drugs exhibit linear kinetics with constant clearance  Plasma concentrations (Cp) decrease exponentially over time: Ct = C0 * e^(-kt)  K is elimination rate constant and represents a fraction of drug eliminated per unit of time  Elimination rate constant: K_el = CL-tot / Vd, units is 1/time (e.g. 0.1/h means 1/10 of the drug is eliminated per hour)  Elimination half-life: Time for plasma concentration to decrease by half, t1/2 = 0.693/ k_el  It predicts time course of drug concentration after starting or stopping infusion  Drug infusion and steady-state: During constant-rate infusion, plasma concentration (C_t) increases towards steady- state (C-ss): C_t = C_ss * (1-e^(-kt)), when infusion stops, concentration falls exponentially: C_t = C_0 * e^(-kt)  Accumulation and elimination: Accumulation curve is inverse of elimination curve, rate constants (k) and half-lives (t1/2) for accumulation and elimination are the same and time to reach steady-state: 1 t1/2 for 50%, 2 t1/2 for 75% and 3 t1/2 for 87.5% etc.  Clinical implications: Longer half-life means longer drug persistence in the body, for chronic administration, longer half-life means longer to reach steady-state and loading dose may be used to quickly achieve therapeutic concentration  Loading dose = determined by volume of distribution and used rapidly to reach desired plasma concentration  R E P E AT E D D O S I N G  Therapeutic Drug Dosing: drugs are given as repeated doses rather than single injections or constant infusion  Repeated injections: Each dose (Q) creates a pattern of plasma concentration (Cp) changes and mean concentration rises to mean steady-state level with oscillations through a range of Q/ Vd  Intravenous injections lead to complete absorption, with Cp oscillating through a range of Q/Vd  Frequency and Dose size: Smaller and more frequent dosing reduces concentration swings and mimic continuous infusion more closely  Exact dosage schedule does not affect mean steady-state concentration or rate of its approach  Steady-state achievement: Steady state is typically reached after 3-5 half-lives and larger initial loading dose can speed up reaching steady-state, useful in urgent clinical situations  Clinical examples: Loading dose used for drugs with long half-lives in urgent cases (e.g. digoxin or amiodarone for cardiac dysrhythmias)  E F F E C T VA R I AT I O N I N RAT E O F ABSORPTION  Slow absorption from the gut or injection site resembles a variable-rate infusion into the bloodstream in compartmental reading.  Transfer of drug to central compartment approximated by rate constant  Effect on plasma concentration: Slow absorption delays and reduces peak concentration (makes it less sharp)  Peak concentration is lower and appears with slower absorption  Constant rate infusion: Dosage forms releasing drug at constant rate mimic constant- rate infusion and plasma concentration declines with the same half-life after absorption is complete, regardless of absorption rate  AUC and rate of absorption: AUC is proportional to the total amount of drug in plasma compartment, regardless of absorption rate. Incomplete absorption or pre-systemic metabolism reduces AUC after oral administration  Changes in absorption rate do not affect AUC if absorption is complete  Effect on steady-state plasma concentration: Relation between rate of administration and steady-state plasma concentration remains unaffected by k-abs. Slower absorption reduces rate of increase in plasma concentration with each dose, but steady state concentration remains the same  M O R E C O M P L I C AT E D K I N E T I C M O D E L S  Single-Compartment PK model: It assumes rates of absorption, metabolism and excretion are directly proportional to drug concentration in the compartment  Simplified representation useful illustrating basic principles but oversimplifies physiological activity  Physiological differences: Different parts of the body (e.g. brain, body fat, muscle) have distinct characteristics affecting drug distribution (blood supply, partition coefficients, capillary permeability)  These differences are ignored by single compartment model but significantly impact drug distribution and action  Complex models: Mathematical analysis of more complex models has been conducted to account for physiological variations and these models consider the complexities of the drug distribution more accurately  Two-Compartment Model: A separate peripheral compartment representing tissues, communicating with the ‘central’ plasma compartment, closely resembles a physiological situation compared to single-compartment model. It is useful conceptually, without excessive complications  Non-Compartmental analysis is mainly used in drug-development over compartmental models for its simplicity and practicality T W O - C O M PA RT M E N T M O D E L S  Represents tissues as peripheral compartment communicating with the central plasma compartment  Drug molecules enter and leave the peripheral compartment only through the central compartment  Effect of the second compartment: Introduces second exponential component into the predicted time course of plasma concentration and comprises of fast and slow phases, often observed experimentally  Experimental observations: Pattern is clearer when the concentration data is plotted semi-logarithmically  Fast Phase (a Phase): Represents redistribution of drug from plasma to tissues, lowering plasma concentration rapidly and if transfer between compartments is fast compared to elimination, a-phase is prominent (important)  Measurement significance: Plasma concentration at the end of the fast phase provides a measure of combined distribution volumes of both compartments  Half-time for slow phase (B-phase) eliminates elimination rate constant (k_el)  Challenges: Rapid metabolism or excretion may not separate a & B phases clearly  Calculation of separate Vd and K_el values for each phase is difficult and issues arise with very fat-soluble drugs, where lumping all peripheral tissues together is unrealistic S AT U R AT I O N K I N E T I C S  Some drugs like ethanol, phenytoin and salicylate exhibit saturation kinetics  Removal from plasma occurs at a constant rate independent of plasma concentration, initially linear  Mechanism: Saturation occurs when a carrier or enzyme becomes saturated, causing the rate of elimination to approach a constant value as drug concentration increases  Consequences: Duration of action depends more on dose compared to drugs without metabolic saturation  Metabolic saturation is when therapeutic concentrations overwhelm the metabolising enzymes (i.e. saturation) and adjusting the drug dose to see enzymes can eventually become saturated and no further increase in the rate of metabolism can occur.  Relationship between dose and steady-state plasma concentration is steep and unpredictable  Maximum rate of metabolism limits drug administration rate; exceeding this rate can lead to indefinite increase in the drug amount in the body  Clinical implications: Steady-state plasma concentrations vary widely and unpredictably with dose  Variations in metabolism rate, such as through enzyme induction, lead to disproportionate changes in the plasma concentration  Drugs with saturation kinetics are less predictable clinically compared to those with first-order kinetics  Such drugs may be rejected during development if pharmacologically similar candidates with first order kinetics are available  Some drugs exhibit non-linearity where plasma concentration increases less in proportion with dose increment, often due to absorption carrier saturation or formulation issues P O P U L AT I O N P H A R M A C O K I N E T I C S  Sometimes necessary to obtain PK data in patient populations, like chronically ill children  Studies in children rely on opportunistic sampling during clinical care, leading to limitations in data quality and sample quantity  Challenges: Analysing PK data from diverse individuals poses challenges and traditional methods involve fitting data without considering individual differences or fitting everyone's data separately and then combining parameter estimates  Non-linear Mixed Effects Modelling (NONMEM): A better method for population PK analysis and accounts for both inter-individual variability and variability within individuals over time  Clinical relevance: Population PK analysis helps predict individual variation in both clinical trials and clinical practice  Mathematical modelling is essential for understanding how population studies relate to individual patients  Complexities: Uncertainties and statistical technicalities in population PK are significant and further reading for people who are interested: Sheiner et al. (1997) (this is in rang and dales chapter on PK) L I M I TAT I O N S O F P K A P P R O A C H  Even simple PK models can lead to a proliferation of parameters, making interpretation complex  Monitoring drug concentrations in plasma doesn’t always effectively reduce variability in drug response  Assumptions underpinning plasma concentration- response relationships: 1) Precise relation to Target concentration: Plausible for drugs acting directly in the bloodstream or on cell membrane targets Less likely for drugs acting on nuclear receptors or with active metabolites 2) Unique dependence target concentration: Not true for drugs forming stable covalent attachments or producing delayed effects Examples: aspirin, clopidogrel, some monoamine oxidase inhibitions and PPI’s Clinical implications: Monitoring plasma concentrations may not reflect local concentrations in the brain due to BBB For drugs acting in the brain, plasma concentration monitoring is often not clinically useful Drugs effects may persist beyond their presence in plasma or may be time-dependent, altering the concentration- effect relationship Examples: antidepressants, opioids and corticosteroids