Population Pharmacokinetics, Evaluation of methylphenydate on ADHD, Pop PK Model

Published on: **Mar 4, 2016**

Published in:
Health & Medicine

- 1. Population Pharmacokinetics Mr. T.S. Mohamed Saleem M.Pharm., Ph.D Assistant Professor & Head
- 2. Introduction T.S.M. Saleem-Department of 26-11-2014 Pharmacology 2 Pharmacokinetic studies based on a traditional intensive design model are usually conducted using carefully selected volunteer subjects, a controlled experimental design, and collection of multiple blood samples. After measurement of drug and metabolite concentrations in all samples, pharmacokinetic models are applied to determine parameters such as elimination half-life, volume of distribution, and clearance. During the new drug development process, a series of pharmacokinetic studies are conducted to determine the influence of major disease states or experimental conditions hypothesized to affect drug disposition. Such factors might include age, gender, body weight, ethnicity, hepatic and renal disease, coadministration of food, and various drug interactions.
- 3. Introduction (Cont……) T.S.M. Saleem-Department of 26-11-2014 Pharmacology 3 Classical pharmacokinetic studies can quantitate the effects of anticipated influences on drug disposition under controlled circumstances, but cannot identify the unexpected factors affecting pharmacokinetics. A number of examples of altered drug pharmacokinetics became apparent in the patient care setting only in the postmarketing phase of extensive clinical use. Examples include the digoxin-quinidine interaction, altered drug metabolism due to cimetidine, and the ketoconazole-terfenadine interaction.
- 4. Population Pharmacokinetic method T.S.M. Saleem-Department of 26-11-2014 Pharmacology 4 Population pharmacokinetic methodology has developed as an approach to detect and quantify unexpected influences on drug pharmacokinetics. Population pharmacokinetic studies, in contrast to classical or traditional pharmacokinetic studies, focus on the central tendency of a pharmacokinetic parameter across an entire population, and identify deviations from that central tendency in a subgroup of individual patients. Analysis of clinical data using a population approach allows pharmacokinetic parameters to be determined directly in patient populations of interest and allows evaluation of the influence of various patient characteristics on pharmacokinetics. Because the number of blood samples that need to be collected per subject is small, this approach is often suitable for patient groups unable to participate in traditional pharmacokinetic studies requiring multiple blood samples (e.g.,) neonates, children, critically ill patients, or individuals who are not able to provide informed consent
- 5. Methylphenidate Pharmacokinetics A study of methylphenidate (MP) pharmacokinetics in children. Study design may not be appropriate for ethical and practical reasons. Participating subjects were 273 children aged 5 to 18 years having a primary diagnosis of attention deficit/hyperactivity disorder (ADHD). They had been receiving MP at a fixed dosage level for at least 4 weeks, and were under treatment for at least 3 months. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 5
- 6. Children meeting the eligibility criteria had an initial screening visit, at which one parent or a legal guardian provided written informed consent, and the child provided assent. Demographic characteristics were recorded, including the dosage of MP, the usual times for individual doses, and the duration of treatment. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 6
- 7. The second visit, which followed shortly, was a blood sampling day. The time and size of the last MP dose, and of any other medication received that day or during the prior 2 weeks, were recorded. A 5-mL whole blood sample was obtained by venipuncture. This sample was immediately centrifuged, and a 2- mL aliquot of plasma was removed for subsequent determination of MP concentrations by a liquid chromatography/mass spectroscopy/mass spectroscopy (LC/MS/MS) assay. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 7
- 8. Analysis of data The identified independent variables were age, sex, body weight, size of each dose, and time of sample relative to the most recent dose. The pharmacokinetic model was a one-compartment model with first-order absorption and first-order elimination, under the assumption that all subjects were at steady state (Fig. 1). T.S.M. Saleem-Department of 26-11-2014 Pharmacology 8
- 9. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 9
- 10. The overall model was specifically modified for each of the 273 subjects to incorporate the individually applicable independent variables, as well as the dosage schedule (b.i.d. or t.i.d.). Individual values of continuous variables (t time sample taken relative to the first dose; C plasma MP concentration) were fitted to a single set of iterated variables using unweighted nonlinear regression (Fig.1). When the time between first and second doses, or between second and third doses, was not available, the mean value was assigned based on cases in which the data were available. For the b.i.d. dosage, the mean interval was 4.3 hours. For the t.i.d. dosage, the mean intervals were 4.1 and 3.7 hours, respectively. As is customary, clearance was assumed to be proportional to body weight. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 10
- 11. Results The total daily dose of MP was significantly lower in subjects receiving MP b.i.d. (n 109) compared to subjects on a t.i.d. schedule (n 164); the mean total daily dosages in the two groups were 25 and 39.3 mg, respectively (p .001). Within each group, clinician choices of total daily dosages were influenced by body weight, as mean total daily dose increased significantly with higher body weights. However, the association of body weight with mean plasma concentration was not significant for the b.i.d. dosage group, and of only borderline significance (.05 p .1) for the t.i.d group. This finding is consistent with the underlying assumption that clearance is proportional to body weight. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 11
- 12. Age was significantly correlated with body weight (r 0.54, p .001) and with height (r 2 0.77). Height and body weight also were significantly correlated (r 0.77). An acceptable estimate of absorption rate constant could be derived only for the b.i.d. dosing data. The iterated parameter estimate was 1.192/h, corresponding to an absorption half-life of 34.9 minutes. The iterated estimates were 0.154/h for elimination rate constant, corresponding to an elimination halflife of 4.5 hours (relative standard error: 23%). For clearance, the estimate was 90.7 mL/min/kg (relative standard error: 9%). The overall r-square was 0.43 (Fig. 38.2). There were no evident differences in pharmacokinetics attributable to gender. Figure 2 shows predicted plasma MP concentration curves for b.i.d. and t.i.d. dosage schedules, based on the population estimates. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 12
- 13. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 13
- 14. Implication Pharmacokinetically based approaches to the treatment of ADHD with MP are not clearly established. The mean prescribed per dose amount for the whole study population was 0.335 mg/kg per dose (range 0.044-0.568), and 36% of the children received between 0.25 and 0.35 mg/kg per dose. The mean total daily dose was 0.98 mg/kg/day for the entire sample, and increased significantly in association with larger body weight. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 14
- 15. The pharmacokinetic model explained 43% of the variability in plasma MP concentrations during typical naturalistic therapy. The model fit equally well for both genders. Assuming that clearance is proportional to body weight in the context of intercorrelated age and weight allows age, weight, and daily dosage to be used to predict plasma concentrations of MP during clinical use in children. These findings support the value of prescribing MP on a weight adjusted basis. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 15
- 16. Our typical population value of elimination half-life was 4.5 hours, with a confidence interval of 3.1 to 8.1 hours. This estimate somewhat exceeds the usual range of half-life values reported in single-dose kinetic studies of MP. This could reflect the relatively small number of plasma samples from the terminal phase of the plasma concentration curve, upon which reliable estimates of beta are dependent. MP kinetics may also have a previously unrecognized dose-dependent component, in which estimated values of half-life are larger at steady state than following a single dose. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 16
- 17. The single-sample approach described in this study allows relatively noninvasive assessment of pharmacokinetic parameters in a group of children and adolescents under naturalistic circumstances of usual clinical use, when blood sampling is not otherwise clinically indicated. This approach in general can be applied to other special populations such as neonates, the elderly, or individuals with serious medical disease. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 17
- 18. General Methods for Population Pharmacokinetic Modeling Non-Parametric Adaptive Grid and Non-Parametric Bayesian T.S.M. Saleem-Department of 26-11-2014 Pharmacology 18
- 19. Population pharmacokinetic (PK) modeling involves estimating an unknown population distribution based on data from a collection of nonlinear models. A drug is given to a population of subjects. In each subject, the drug’s behavior is stochastically described by an unknown subject-specific parameter vector ∂. This vector ∂ varies significantly (often genetically) between subjects, which accounts for the variability of the drug response in the population. The mathematical problem is to determine the population parameter distribution F (∂) based on the clinical data T.S.M. Saleem-Department of 26-11-2014 Pharmacology 19
- 20. According to FDA “Knowledge of the relationship among concentration, response, and physiology is essential to the design of dosing strategies for rational therapeutics. Defining the optimum dosing strategy for a population, subgroup, or individual patient requires resolution of the variability issues.” T.S.M. Saleem-Department of 26-11-2014 Pharmacology 20
- 21. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 21
- 22. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 22 Traditional Population Population Healthy volounteers Highly selected patients Target patient population (Pediatrics, elderly, AIDS) Study size Small Large or integrated (observational or experimental) Sampling data Dense (typically 1 to 6 time points) following drug administration. Sparse, few samples for many patients Inter-individual Variability Minimized through restrictive criteria Demographics Pathophysiological Concomitant medications Relationships of concentration, PK/PD Limited Extensive, make predictions about future events - steady state concentrations and efficacy. guide dosage adjustments. determine therapeutic window. guide dosage for safety
- 23. Ri kt e Cl Cp 1 T.S.M. Saleem-Department of 26-11-2014 Pharmacology E.g.: A simple Pk model Ri = infusion rate Cl = drug clearance k =elimination rate constant = measurement error, intra-individual 23 error Drug Conc N(0,) Time
- 24. Ri Cp Cp e Ri T.S.M. Saleem-Department of 26-11-2014 Pharmacology 24 Drug Conc Time ss kt ss kt Cp Cl e Cl Cp 1 1
- 25. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 25 PK Model Objectives 1. Provide Estimates of Population PK Parameters (CL, V) - Fixed Effects 2. Provide Estimates of Variability - Random Effects Intersubject Variability Interoccasion Variability (Day to Day Variability) Residual Variability (Intrasubject Variability, Measurement Error, Model Misspecification)
- 26. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 26 PK Model Objectives 3. Identify Factors that are Important Determinants of Intersubject Variability Demographic: Age, Body Weight or Surface Area, gender, race Genetic: CYP2D6, CYP2C19 Environmental: Smoking, Diet Physiological/Pathophysiological: Renal (Creatinine Clearance) or Hepatic impairment, Disease State Concomitant Drugs Other Factors: Meals, Circadian Variation, Formulations
- 27. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 27 PK model Advantages Sparse Sampling Strategy (2-3 concentrations/subject) Routine Sampling in Phase II/III Studies Special Populations (Pediatrics, Elderly) Large Number of Patients Fewer restrictions on inclusion/exclusion criteria Unbalanced Design Different number of samples/subject Target Patient Population Representative of the Population to be Treated
- 28. Population PK modeling approaches can be classified statistically as either parametric or nonparametric. Each can be divided into maximum likelihood or Bayesian methods. parametric maximum likelihood (PML) nonparametric maximum likelihood (NPML) T.S.M. Saleem-Department of 26-11-2014 Pharmacology 28
- 29. Parametric maximum likelihood (PML) Oldest and most traditional. The parameters come from a known, specified probability distribution (the population distribution) with certain unknown population parameters (e.g. normal distribution with unknown mean vector μ and unknown covariance matrix Σ). The first and most widely used software for this approach has been the NONMEM (NONlinear Mixed Effects Modeling) program developed by Sheiner and Beal . There are other parametric maximum likelihood programs currently available, such as Monolix and ADAPT. The ADAPT software also allows for parametric mixtures of normal distributions. Asymptotic confidence intervals can be obtained about these population parameters. Here “asymptotic” means as the number of subjects in the population becomes large. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 29
- 30. Nonparametric maximum likelihood (NPML) The nonparametric maximum likelihood (NPML) approach was initially developed by Lindsay and Mallet. It directly estimates the entire joint distribution. This permits discovery of unanticipated, often genetically determined, nonnormal and multimodal subpopulations, such as fast and slow metabolizers. The NPML approach is statistically consistent . This means that as the number of subjects gets large, the estimate of F given the data converges to the true F. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 30
- 31. NP Adaptive Grid (NPAG) algorithm This method calculates the maximum likelihood estimate of the population distribution with respect to all distributions. Compared with most parametric population modeling methods, NPAG calculates exact, rather than approximate likelihoods, and it easily discovers unexpected sub-groups and outliers T.S.M. Saleem-Department of 26-11-2014 Pharmacology 31
- 32. NP Bayesian (NPB) algorithm The NPB algorithm provides a Bayesian estimate of this totally unknown population distribution, including rigorous (not asymptotic) credibility intervals around all parameter estimates for any sample size. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 32
- 33. THE POPULATION PK/PD MODEL Consider a sequence of experiments where each one consists of a dosage regimen and a set of measurements at several time points on one of N individual subjects. Yi are the observed measurements, e.g. serum concentrations, PD effects. The population analysis problem is to estimate based on the data T.S.M. Saleem-Department of 26-11-2014 Pharmacology 33
- 34. DATA AND INFORMATION REQUIRED FOR POPULATION PK ANALYSIS 1. Data Input 2. Prior Knowledge and Information T.S.M. Saleem-Department of 26-11-2014 Pharmacology 34
- 35. Data input Accurate dosing information and history such as dose formulation, dosage. Plasma/blood concentrations from a validated assay (sparse or dense) Pharmacodynamic measurements and safety profiles (e.g., ECG, side effects) Covariate data – demographics, lab values, concomitant meds, metabolizer status, disease, fasting. Accurate capture of time/date associated with above items T.S.M. Saleem-Department of 26-11-2014 Pharmacology 35
- 36. Prior Knowledge and Information Previous PK information: pharmacokinetic modeling compartmental model, parameter estimates, relative proportion of inter-patient to intrapatient and/or residual. summary statistics. Impact of patient covariates age, body weight, medical conditions. For example, creatinine clearance and drug clearance, much of the drug is eliminated by the kidney without being metabolized (unchanged) or much of the drug undergoes metabolism. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 36
- 37. Conclusions Population modeling is most likely to add value when a reasonable a priori expectation exists that inter-subject kinetic variation may warrant altered dosing for some subgroups in the target population. The population PK approach can be used to estimate population parameters of a response surface model in phase 1 and late phase 2b of clinical drug development, where information is gathered on how the drug will be used in subsequent stages of drug development. The population PK approach can increase the efficiency and specificity of drug development by suggesting more informative designs and analyses of experiments. In phase 1 and, perhaps, much of phase 2b, where patients are sampled extensively, complex methods of data analysis may not be needed. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 37
- 38. Conclusions (Conti………..) The population PK approach can also be used in early phase 2a and phase 3 of drug development to gain information on drug safety (efficacy) and to gather additional information on drug pharmacokinetics in special populations, such as the elderly. This approach can also be useful in post-marketing surveillance (phase 4) studies. Studies performed during phases 3 and 4 of clinical drug development lend themselves to the use of a full population pharmacokinetic sampling study design (few blood samples drawn from several subjects at various time points. This sampling design can provide important information during new drug evaluation, regulatory decision making, T.S.M. Saleem-Department of 26-11-2014 and drug labeling. Pharmacology 38
- 39. T.S.M. Saleem-Department of 26-11-2014 Pharmacology 39