ABSTRACT
To exemplify a systematic and quantitative approach (Biopharmetrics approach) for product quality control with clinical relevance, the study utilized multivariate models to connect raw material attributes of a product to its dissolution obtained in a DOE (design of experiment) and further extended the connection to the in vivo exposures (AUC and Cmax) of the product using an available IVIVC (in vitro/in vivo correlation). Thus, a model for the relationship between in vivo exposure and raw material attributes of the product was established. Based on the model, the exposure ratios between future batches (with various raw material attributes) and the pivotal clinical batches (with confirmed clinical efficacy and safety) were predicted resulting in a set of contour lines in a graphical representation. Among these contour lines, the lines for exposure ratios of 0.8 and 1.2 were defined as lower and upper limits. With the failure edges defined, clinically meaningful limits for the raw material quality control were specified. If the raw material attributes in the future production conform to the specifications, the manufacturing would lead to products bioequivalent to the clinical batches. From these exercises, Biopharmetrics, a subdiscipline of biopharmaceutics, was introduced and its definition, scope, and characteristics were discussed.
Subject(s)
Biopharmaceutics , Pharmaceutical Preparations , Drug and Narcotic Control , Quality Control , Therapeutic EquivalencyABSTRACT
One of the most commonly used methods to establish the clinical relevance of dissolution is to align the dissolution specifications with pivotal clinical batches. The objective of the study was to create edge charts for the dissolution of immediate release (IR) drug products to quantitatively establish the bases for setting clinically relevant and discriminating dissolution specifications and to clarify which stage in the US Pharmacopoeia (USP) <711> acceptance tables should be targeted. The simulations of dissolution data were performed on a batch of IR products with 1,000,000 units. The desired acceptance criterion was Q = 80% of the label claim at 30 min. A total of 110 scenarios for IR data were generated, which included various combinations of two determinants: the batch mean and SD (standard deviation). For each scenario, the dissolution data were tested based on USP three-stage procedures to determine the pass/fail at each stage. This process was repeated 10,000 times. The failure rate at each stage for each scenario was calculated as the percentage of failed replicates across 10,000 trials. Contour plots, named edge charts, were created to demonstrate the relationship between the dissolution failure rates and the two determinants (mean and SD). The edge lines represent the failure rates for the given combinations of the mean and SD. The edge charts can provide a quantitative estimate based on the observed dissolution data and provide fundamental support for recommendations on using USP stage 2 as a target for setting the acceptance limit(s).
Subject(s)
Drug Liberation , Pharmaceutical Preparations/standards , Quality Control , Administration, Oral , Chemistry, Pharmaceutical/standards , Computer Simulation , Pharmaceutical Preparations/chemistry , Pharmacopoeias as Topic/standards , Software , Solubility , United StatesABSTRACT
The present study examines how drug's inherent properties and product design influence the evaluation and applications of in vitro-in vivo correlation (IVIVC) for modified-release (MR) dosage forms consisting of extended-release (ER) and immediate-release (IR) components with bimodal drug release. Three analgesic drugs were used as model compounds, and simulations of in vivo pharmacokinetic profiles were conducted using different release rates of the ER component and various IR percentages. Plasma concentration-time profiles exhibiting a wide range of tmax and maximum observed plasma concentration (Cmax) were obtained from superposition of the simulated IR and ER profiles based on a linear IVIVC. It was found that depending on the drug and dosage form design, direct use of the superposed IR and ER data for IVIVC modeling and prediction may (1) be acceptable within errors, (2) become unreliable and less meaningful because of the confounding effect from the non-negligible IR contribution to Cmax, or (3) be meaningless because of the insensitivity of Cmax to release rate change of the ER component. Therefore, understanding the drug, design and drug release characteristics of the product is essential for assessing the validity, accuracy, and reliability of IVIVC of complex MR products obtained via directly modeling of in vivo data.
Subject(s)
Dosage Forms , Drug Design , Pharmaceutical Preparations/chemistry , Acetaminophen/administration & dosage , Acetaminophen/chemistry , Algorithms , Analgesics, Non-Narcotic/administration & dosage , Analgesics, Non-Narcotic/chemistry , Analgesics, Opioid/administration & dosage , Analgesics, Opioid/chemistry , Anti-Inflammatory Agents, Non-Steroidal/administration & dosage , Anti-Inflammatory Agents, Non-Steroidal/chemistry , Area Under Curve , Biological Availability , Computer Simulation , Delayed-Action Preparations , Intestinal Absorption , Models, Chemical , Naproxen/administration & dosage , Naproxen/chemistry , Reproducibility of Results , Tramadol/administration & dosage , Tramadol/chemistryABSTRACT
PURPOSE: To investigate how likely two extended release formulations are to be bioequivalent when they demonstrate f2 similarity. METHOD: Dissolution profiles were simulated using the Weibull model and varying model parameters around those of a reference profile. The f2 values were calculated for the comparisons of each simulation with the reference profile. The in vivo inputs obtained from an in vitro-in vivo correlation model were convolved with a unit impulse response function. The AUC, Cmax, and Tmax from each simulated in vivo concentration profile were compared to the reference profile. The AUCR (AUC ratio) and CmaxR (Cmax ratio) were determined. The consistency between f2 and bioequivalence was investigated. RESULTS: The relationships between AUCR, CmaxR, f2 and the Weibull model parameters demonstrate that the bioequivalence regions enclosed by the contour lines of 80% and 125% of AUCR and CmaxR were generally close to the regions enclosed by the f2 = 50 contour line, but did not exactly match, especially when Dmax and B deviated from the reference values. CONCLUSIONS: When f2 is used for in vitro dissolution profile comparison, the completeness of the dissolution profiles should not differ more than 10%, and the shapes of the dissolution profiles should not be significantly different.
Subject(s)
Delayed-Action Preparations/chemistry , Pharmaceutical Preparations/administration & dosage , Animals , Area Under Curve , Computer Simulation , Humans , Models, Biological , Models, Chemical , Pharmaceutical Preparations/chemistry , Solubility , Therapeutic EquivalencyABSTRACT
Applying a comedication (COMD) covariate to apparent clearance (CL(app) = CL/F) is a common practice when using population pharmacokinetics (PopPK) to study metabolism-based drug-drug interactions (DDI). This study evaluates the importance of independently applying COMD to F and CL to account for DDI at the level of first-pass metabolism. A known DDI between single oral doses of the CYP3A substrate midazolam (5 mg) and the inhibitor ketoconazole (400 mg) was simulated using a physiologically based pharmacokinetic simulator SimCyp in virtual subjects. The simulated midazolam data were analyzed by PopPK method under the following scenarios by applying COMD effect to (1) CL(app) only, (2) CL and F, and (3) CL(app) and apparent volume of distribution (V(app) = V/F), assuming V is unchanged. The mean simulated degree of interaction, measured by midazolam AUC ratio with and without ketoconazole (AUCR), was 10.28. Scenario 1 underestimated AUCR. When COMD was independently applied to F and V(app) in scenarios 2 and 3, lower objective function values of the PopPK analysis and more accurate AUCR estimates were achieved. AUCR estimates were also dependent on sampling. The authors conclude that when significant inhibition of the first-pass metabolism of the substrate is anticipated, COMD effects should be applied to both CL and F in PopPK analysis.
Subject(s)
Drug Interactions , Models, Biological , Pharmacokinetics , Algorithms , Biological Availability , Biotransformation , Computer Simulation , Cytochrome P-450 CYP3A/metabolism , Cytochrome P-450 CYP3A Inhibitors , Humans , Ketoconazole/pharmacology , Metabolic Clearance Rate , Midazolam/blood , Midazolam/pharmacokineticsABSTRACT
BACKGROUND AND OBJECTIVE: Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major concerns is the change of exposures, including the rate and extent of drug absorption, distribution, metabolism and elimination. To address the concerns, a common practice is to measure and report the differences between the exposure in the presence and in the absence of concomitant medication (COMED). The area under the plasma concentration versus time curve (AUC), maximum plasma concentration (C(max)) and time to reach the C(max) (t(max)) changes are usually measured in DDI studies. A usual observation is the different extents of changes among AUC, C(max) and t(max), which may raise concerns in certain therapeutic areas or some special agents. The objective of this study was to investigate the variation among changes of AUC, C(max) and t(max) in DDI studies, and its pharmacokinetic manifestation. DATA SOURCES: Based on a list of DDI results from the literature, with the assumptions that the primary parameters of a drug of interest were altered during a DDI, two sets of simulated data were generated according to a single oral dose, one-compartment model. The first set including 24 cases with different half-lives and absorption constants (k(a)) considered the exposure changes upon independent variation of bioavailability (F), clearance (CL), volume of distribution (V(d)) and k(a) up to 50-fold increases or decreases. The second set considered the exposure changes with simultaneous variation of F, CL, V(d), and k(a) within 5-fold range (increase or decrease) for a case selected from the first set. STUDY SELECTION, DATA EXTRACTION AND SYNTHESIS: Parameter fold changes (defined in a fashion showing fold increase or fold decreases, including CL fold change, F fold change, V(d) fold change and k(a) fold change) and exposure changes (AUC fold change, C(max) fold change, t(max) fold change and fold change difference [AUC fold change - C(max) fold change]) were used to generate plots demonstrating various relationships between parameter fold changes and exposure changes. Based on the observations that AUC was influenced by CL and F, C(max) was affected by all four parameters, t(max) was mainly determined by CL and k(a), F did little for t(max) and k(a) was unrelated to AUC, a chart was created for DDI pattern recognition. CONCLUSION: An approach, named DDI pattern recognition, is proposed for didactical purposes. It provides a quick initial estimate for interpreting the DDI results based on the exposure changes. This approach entails the following stages: (i) performing a drug interaction study; (ii) calculating the exposure changes in the presence of COMED compared to those in the absence of COMED, and the fold change difference; (iii) selecting the parameter fold changes that may play important roles in a specific DDI, by estimating their possible ranges; and (iv) interpreting the DDI by integrating all the information available, such as the possible mechanism involved. A quicker and better understanding about the processes, which dominate a DDI, has been achieved using this approach by focusing on integration of all information available and mechanistic interpretation.
Subject(s)
Drug Interactions , Models, Statistical , Pattern Recognition, Automated/methods , Pharmaceutical Preparations/metabolism , Pharmacokinetics , Computer Simulation , HumansABSTRACT
The purpose of this study is to compare and evaluate logistic regression and time-to-event analysis, 2 commonly used methods for exposure-adverse event (AE) analyses. An AE data set selected from clinical trials is analyzed by both methods and the results are compared. The parameter estimates, odds ratios for logistic regression, and hazard ratios for time-to-event analysis for each AE are compared and further analyzed. In a data set involving 822 patients, 25 AEs are analyzed. A linear relationship is demonstrated between the parameter estimates from the 2 methods and between the odds ratios and hazard ratios. The small differences between the 2 analyses are related to the lower rate of the events and the weaker effects of the risk factors. Although the 2 methods can make predications for the risks, the severity, duration, and recurrence are not well defined. AE time profiles showing the onset, duration, and offset of AEs are important for risk assessment and management. Both analyses can provide information about exposure-AE relationship, and the results from the 2 analyses are consistent in most cases. One should not use the logistic model when length of follow-up varies because of biased estimates. The application of the 2 methods should be combined with AE profiling.
Subject(s)
Adverse Drug Reaction Reporting Systems/statistics & numerical data , Humans , Logistic Models , Odds Ratio , Proportional Hazards Models , Risk Assessment , Risk Factors , Time FactorsABSTRACT
The value of quantitative thinking in drug development and regulatory review is increasingly being appreciated. Modeling and simulation of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression is often referred to as the pharmacometrics analyses. The objective of the current report is to assess the role of pharmacometrics at the US Food and Drug Administration (FDA) in making drug approval and labeling decisions. The New Drug Applications (NDAs) submitted between 2000 and 2004 to the Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed. For those NDA reviews that included a pharmacometrics consultation, the clinical pharmacology scientists ranked the impact on the regulatory decision(s). Of about a total of 244 NDAs, 42 included a pharmacometrics component. Review of NDAs involved independent, quantitative evaluation by FDA pharmacometricians, even when such analysis was not conducted by the sponsor. Pharmacometric analyses were pivotal in regulatory decision making in more than half of the 42 NDAs. Of the 14 reviews that were pivotal to approval related decisions, 5 identified the need for additional trials, whereas 6 reduced the burden of conducting additional trials. Collaboration among the FDA clinical pharmacology, medical, and statistical reviewers and effective communication with the sponsors was critical for the impact to occur. The survey and the case studies emphasize the need for early interaction between the FDA and sponsors to plan the development more efficiently by appreciating the regulatory expectations better.