Playing Hide-and-Seek with Tyrosine Kinase Inhibitors: Can We Overcome Administration Challenges?

Tyrosine kinase inhibitors (TKIs) have demonstrated significant efficacy against various types of cancers through molecular targeting mechanisms. Over the past 22 years, more than 100 TKIs have been approved for the treatment of various types of cancer indicating the significant progress achieved in this research area. Despite having significant efficacy and ability to target multiple pathways, TKIs administration is associated with challenges. There are reported inconsistencies between observed food effect and labeling administration, challenges of concomitant administration with acid-reducing agents (ARA), pill burden and dosing frequency. In this context, the objective of present review is to visit administration challenges of TKIs and effective ways to tackle them. We have gathered data of 94 TKIs approved in between 2000 and 2022 with respect to food effect, ARA impact, administration schemes (food and PPI restrictions), number of pills per day and administration frequency. Further, trend analysis has been performed to identify inconsistencies in the labeling with respect to observed food effect, molecules exhibiting ARA impact, in order to identify solutions to remove these restrictions through novel formulation approaches. Additionally, opportunities to reduce number of pills per day and dosing frequency for better patient compliance were suggested using innovative formulation interventions. Finally, utility of physiologically based pharmacokinetic modeling (PBPK) for rationale formulation development was discussed with literature reported examples. Overall, this review can act as a ready-to-use-guide for the formulation, biopharmaceutics scientists and medical oncologists to identify opportunities for innovation for TKIs.

Novel application of PBBM to justify impact of faster dissolution on safety and pharmacokinetics - a case study and utility in regulatory justifications.

Physiologically based biopharmaceutics modelling (PBBM) was recognised as potential approach for biopharmaceutics applications. However, PBBM to justify safety is an unexplored area.In this manuscript, we elucidated PBBM application for safety justification. Product DRL is a generic extended release tablet containing an anti-epileptic narrow therapeutic index (NTI) drug. During dossier review, regulatory agency requested to evaluate the impact of faster dissolution profiles observed during stability on safety aspects. In order to justify, PBBMbased strategy was adapted.Model was validated and population simulations were performed for reference and test formulations and the predictions matched with clinical outcome. The model was found to be sensitive to dissolution changes and hence applied for the prediction of stability batches exhibiting faster dissolution profiles, virtually generated profiles at lower and upper specifications. The maximum predicted plasma levels were well below the reported safety levels, thereby demonstrating safety of the product.Overall, a novel application of PBBM to justify safety was demonstrated. Similar justifications using PBBM and linking with safety can be adopted where safety can be impacted due to aggravated dissolution profiles. Such justifications have potential to avoid clinical safety studies and helps in faster approval of drug product.

Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software's: Impact of Variability, Sample Size and Number of Bootstraps.

Dissolution profiles comparison is an important element in order to support biowaivers, scale-up and post approval changes and site transfers. Highly variable dissolution can possess significant challenges for comparison and f2 bootstrap approach can be utilized in such cases. However, availability of different types of f2 and confidence intervals (CI) methods indicates necessity to understand each type of calculation thoroughly. Among all approaches, bias corrected and accelerated (BCa) can be an attractive choice as it corrects the bias and skewness of the distribution. In this manuscript, we have performed comparison of highly variable dissolution data using various software's by adopting percentile and BCa CI approaches. Diverse data with different variability's, number of samples and bootstraps were evaluated with JMP, DDSolver, R-software, SAS and PhEq. While all software's yielded similar observed f2 values, differences in lower percentile CI was observed. BCa with R-software and JMP provided superior lower percentile as compared to other computations. Expected f2 recommended by EMA has resulted as stringent criteria as compared to estimated f2. No impact of number of bootstraps on similarity analysis was observed whereas number of samples increased chance of acceptance. Variability has impacted similarity outcome with estimated f2 but chance of acceptance enhanced with BCa approach. Further, freely available R-software can be of attractive choice due to computation of various types of f2, percentile and BCa intervals. Overall, this work can enable regulatory submissions to enhance probability of similarity through appropriate selection of number of samples, technique based on variability of dissolution data.

Power of integrating PBPK with PBBM (PBPK-BM): a single model predicting food effect, gender impact, drug-drug interactions and bioequivalence in fasting & fed conditions.

Over the past few years, PBPK and PBBM modelling have proven their significance in drug development. PBPK modelling is traditionally used to predict drug-drug interactions, exposures in special populations whereas PBBM modelling is a part of PBPK modelling that is used for a range of biopharmaceutics applications.Because of these differences in utilities, often PBPK and PBBM models are developed separately. When both models are combined, they serve multiple purposes through unified model. In the present case, an integrated PBPK-PBBM model for an IR product has been utilised for bioequivalence prediction in fasting & fed conditions, evaluating gender impact and food effect, prediction of drug-drug interactions.Model was built using physicochemical properties, enzymes and transporter kinetics, bio-predictive dissolution and has been validated with passing and failed pilot BE studies. The validated model predicted pivotal bioequivalence outcomes in fasting & fed conditions accurately, predicted gender impact and food effect in line with literature. Drug-drug interactions arising from transporter and metabolising enzymes were predicted accurately.Overall, this work demonstrates the utility of combining PBPK and PBBM model that can yield a single model which can be used for multiple purposes, regulatory justifications and can reduce regulatory review timelines.

Biopharmaceutics Risk Assessment-Connecting Critical Bioavailability Attributes with In Vitro, In Vivo Properties and Physiologically Based Biopharmaceutics Modeling to Enable Generic Regulatory Submissions.

Quality risk assessment following ICH Q9 principles is an important activity to ensure optimal clinical efficacy and safety of a drug product. Typically, risk assessment is focused on product performance wherein critical material attributes, formulation variables, and process parameters are evaluated from a manufacturing perspective. Extending ICH Q9 principles to biopharmaceutics risk assessment to identify factors that can impact in vivo performance is an upcoming area. This is evident by recent regulatory trends wherein a new term critical bioavailability attributes (CBA) has been coined to identify such factors. Although significant work has been performed for biopharmaceutics risk assessment for new molecules, there is a need for harmonized biopharmaceutics risk assessment workflow for generic submissions. In this manuscript, we attempted to provide a framework for performing biopharmaceutics risk assessment for generic regulatory submissions. A detailed workflow for performing biopharmaceutics risk assessment includes identification of initial CBA (iCBA), their confirmatory evaluation followed by definition of the control strategy. Tools for biopharmaceutics risk assessment, i.e., bio-discriminatory dissolution method and physiologically based biopharmaceutics modeling (PBBM) were discussed from a practical perspective. Furthermore, a case study for CBA evaluation using PBBM modeling for an extended-release product for regulatory submission has been described using the proposed workflow. Finally, future directions of integrating CBA evaluation, biopharmaceutics risk assessment to the FDA Knowledge Aided Structured Assessment (KASA) initiative, the necessity of risk assessment templates, and knowledge sharing between industry and academia are discussed. Overall, the work described in this manuscript can facilitate and provide guidance for biopharmaceutics risk assessment for generic submissions.

Best Practices for Integration of Dissolution Data into Physiologically Based Biopharmaceutics Models (PBBM): A Biopharmaceutics Modeling Scientist Perspective.

Dissolution is considered as a critical input into physiologically based biopharmaceutics models (PBBM) as it governs in vivo exposure. Despite many workshops, initiatives by academia, industry, and regulatory, wider practices are followed for dissolution data input into PBBM models. Due to variety of options available for dissolution data input into PBBM models, it is important to understand pros, cons, and best practices while using specific dissolution model. This present article attempts to summarize current understanding of various dissolution models and data inputs in PBBM software's and aims to discuss practical challenges and ways to overcome such scenarios. Different approaches to incorporate dissolution data for immediate, modified, and delayed release formulations are discussed in detail. Common challenges faced during fitting of z-factor are discussed along with novel approach of dissolution data incorporation using P-PSD model. Ways to incorporate dissolution data for MR formulations using Weibull and IVIVR approaches were portrayed with examples. Strategies to incorporate dissolution data for DR formulations was depicted along with practical aspects. Approaches to generate virtual dissolution profiles, using Weibull function, DDDPlus, and time scaling for defining dissolution safe space, and strategies to generate virtual dissolution profiles for justifying single and multiple dissolution specifications were discussed. Finally, novel ways to integrate dissolution data for complex products such as liposomes, data from complex dissolution systems, importance of precipitation, and bio-predictive ability of QC media for evaluation of CBA's impact were discussed. Overall, this article aims to provide an easy guide for biopharmaceutics modeling scientist to integrate dissolution data effectively into PBBM models.

Utility of Physiologically Based Biopharmaceutics Modeling (PBBM) in Regulatory Perspective: Application to Supersede f2, Enabling Biowaivers & Creation of Dissolution Safe Space.

Product DRL is a generic IR tablet formulation with BCS Class-III API, available in two strengths: 50mg & 100mg. The reference and test formulations have salt-A & salt-B of API but both products were bioequivalent based on the in vivo bioequivalence study conducted for higher strength 100mg. While leveraging the generic product to different market, the reference product from other market showed slower release than generic formulation resulting in f2<50 in pH 6.8 for both 50mg and 100mg, because of which waiver for BE study couldn't be granted. To support f2 mismatch at 100mg, 50mg and to facilitate biowaiver of 50mg, a Gastroplus® PBBM model was developed & validated. Virtual bioequivalence trials were performed using the slower dissolution profile of other market reference. It was demonstrated that despite slower dissolution, bioequivalence was achieved for test product against other market reference for 50mg & 100mg strengths. Additionally, dissolution safe space was created using virtual dissolution profiles, which indicated that when >85% released up to 60 min there is no impact on bioequivalence. Overall, for molecules with permeability controlled absorption (i.e. BCS-III), very rapid dissolution criteria can be relaxed by defining dissolution safe space thereby enabling more waivers in future.

Simplified Model-Dependent and Model-Independent Approaches for Dissolution Profile Comparison for Oral Products: Regulatory Perspective for Generic Product Development.

Dissolution profile comparison among different formulations plays a critical role during new drug as well as generic product development. In the generic product development, dissolution profile comparison is a mandate for biowaivers (BCS-based, for lower strengths and IVIVC-based biowaivers) and also from quality control perspective. Even though traditionally similarity factor or f2 is used as a metric for dissolution profile comparison, it comes with multiple limitations and requirements (e.g., number of time points and variability). To overcome this, regulatory agencies suggested model-independent (e.g., MSD) and model-dependent (e.g., zero order, Weibull) dissolution profile comparison methods. Although most of regulatory guidance documents mention about such approaches, their usage in reality is limited probably due to lack of clear, detailed, and step-wise procedure. In this context, the present article describes simplistic yet detailed procedures of dissolution profile comparison with case studies covering generic product development scenario's from a regulatory perspective. Detailed review of regulatory guidances from various agencies was made along with examples of such approaches in regulatory submissions. Data from three formulations-Formulations A, B, and C-were utilized to perform dissolution profile comparison using MSD, zero-order, and Weibull release profile-based comparisons. Dissolution profile comparisons were made using all of these three approaches complying with regulatory requirements. These examples demonstrated value and utility of these approaches and the simplified and detailed procedure explained in this manuscript can be adapted for generic product applications.

Development of an In Vitro-In Vivo Correlation for Sitagliptin and Metformin Prolonged-release Tablet Formulations

Objectives: The objective of this study was to establish and validate an in vitro-in vivo correlation (IVIVC). To investigate the safety of a fixed-dose combination (FDC) versus the reference formulations (Januvia® 100 mg Filmtabletten co-administered with Glucophage® SR 1000 mg prolonged-release tablets), a bioequivalence study was conducted in the fasted and fed states, and the data generated were used to establish the correlation. Materials and Methods: The formulations used in the bioequivalence study were a FDC (sitagliptin hydrochloride equivalent to 100 mg of sitagliptin and metformin hydrochloride 1000 mg prolonged release) and Januvia® 100 mg co-administered with Glucophage® SR 1000 mg. The plasma profiles from the bioequivalence study and respective dissolution data were then utilized to establish "level A" IVIVC. The procedure comprises pharmacokinetic modeling to derive the empirical constants for further use in deconvolution and convolution procedures. Levy plots were constructed to understand the relationship between in vitro and in vivo properties. The internal and external predictabilities were evaluated by comparing the predicted pharmacokinetics with the observed values from the bioequivalence study. Results: The formulations showed approximately 91%-95% and 89%-91% dissolution, respectively in fasted and fed-state dissolution media for sitagliptin. The dissolution of metformin was 96%-98% and 89%-95%, respectively, in fasted and fed-state media. The regression coefficients of all the Levy plots were more than 0.900, indicating a linear correlation between in vitro release and in vivo parameters. The prediction error value of internal and external predictabilities was below 10 and met the US Food and Drug Administration criteria. Therefore, it can be stated that the correlation models are validated and can be used for predictions and to setting the dissolution specifications. The safety and tolerability of the FDC was found to be superior to those of the reference formulations, as fewer adverse events occurred following administration of the FDC. Conclusion: Correlation models can be useful for the prediction of FDCs during the management life cycle of the product. The models can also serve as a surrogate for in vivo studies. The FDC was tolerable, and the adverse events were mild and similar to those observed with the reference products. Therefore, the FDC is safe for use in human subjects.

Dissolution studies and quantification of meloxicam in tablet dosage form by spectrophotometry.

Two simple and inexpensive UV spectrophotometric methods were developed for the quantification and dissolution studies of meloxicam in tablet dosage forms. Meloxicam was estimated at 365nm and 360nm in Method I and Method II, respectively. The calibration curve was linear over a concentration range from 2.0 to 12.0µg/ml for both methods. The limit of detection and limit of quantitation were found to be 0.12µg/ml and 0.38µg/ml, 0.09µg/ml and 0.27µg/ml for Method I and Method II, respectively. The percentage recoveries of meloxicam were found to be 99.68 to 100.61% and 99.11 to 100.96% for Method I and Method II, respectively. It was concluded that the developed methods are precise, accurate and were successfully applied for the estimation of meloxicam in pharmaceutical formulations and in vitro dissolution studies.