(2016) Utility of a human FcRn transgenic mouse model in drug discovery for early assessment and prediction of human pharmacokinetics of monoclonal antibodies, mAbs, 8:6, 1064-1078, DOI: 10.1080/19420862.2016 To link to this article: https://doi.org/10. 1080/19420862.2016 ABSTRACTTherapeutic antibodies continue to develop as an emerging drug class, with a need for preclinical tools to better predict in vivo characteristics. Transgenic mice expressing human neonatal Fc receptor (hFcRn) have potential as a preclinical pharmacokinetic (PK) model to project human PK of monoclonal antibodies (mAbs). Using a panel of 27 mAbs with a broad PK range, we sought to characterize and establish utility of this preclinical animal model and provide guidance for its application in drug development of mAbs. This set of mAbs was administered to both hemizygous and homozygous hFcRn transgenic mice (Tg32) at a single intravenous dose, and PK parameters were derived. Higher hFcRn protein tissue expression was confirmed by liquid chromatography-high resolution tandem mass spectrometry in Tg32 homozygous versus hemizygous mice. Clearance (CL) was calculated using non-compartmental analysis and correlations were assessed to historical data in wild-type mouse, non-human primate (NHP), and human. Results show that mAb CL in hFcRn Tg32 homozygous mouse correlate with human (r 2 D 0.83, r D 0.91, p < 0.01) better than NHP (r 2 D 0.67, r D 0.82, p < 0.01) for this dataset. Applying simple allometric scaling using an empirically derived best-fit exponent of 0.93 enabled the prediction of human CL from the Tg32 homozygous mouse within 2-fold error for 100% of mAbs tested. Implementing the Tg32 homozygous mouse model in discovery and preclinical drug development to predict human CL may result in an overall decreased usage of monkeys for PK studies, enhancement of the early selection of lead molecules, and ultimately a decrease in the time for a drug candidate to reach the clinic.

The posterior predictive approach for multiple response surface optimization presented by Peterson [7] is used to identify a region of process operating conditions where all quality attributes of the product are highly likely to meet specifications. The approach consists of calculating the probability that future responses will meet specification over a multidimensional grid of operating conditions. Examples from the pharmaceutical industry are used to show how the method is applied to statistically designed experiments and the results are used to generate reliability surface plots. The approach supplements traditional analysis and optimization techniques with calculated values that capture the maturity of the process under development, and provide a useful figure of merit in the definition of Design Space [5]. Also considered is the distinction between determining a Design Space to meet the specifications of critical quality attributes (CQA's) [2] for the active pharmaceutical ingredient (API), and a reliable operating region (ROR) that also satisfies desirable manufacturing attributes, such as cost, yield, or throughput. A Bayesian posterior predictive approach offers benefits over traditional frequentist approaches to optimization. The traditional approaches, such as desirability functions or overlapping contours, do not account for model parameter uncertainty and the correlation of the responses at fixed operating conditions.

Various approaches have been applied to optimize biological product fermentation processes and define design space. In this article, we present a stepwise approach to optimize a Saccharomyces cerevisiae fermentation process through risk assessment analysis, statistical design of experiments (DoE), and multivariate Bayesian predictive approach. The critical process parameters (CPPs) were first identified through a risk assessment. The response surface for each attribute was modeled using the results from the DoE study with consideration given to interactions between CPPs. A multivariate Bayesian predictive approach was then used to identify the region of process operating conditions where all attributes met their specifications simultaneously. The model prediction was verified by twelve consistency runs where all batches achieved broth titer more than 1.53 g/L of broth and quality attributes within the expected ranges. The calculated probability was used to define the reliable operating region. To our knowledge, this is the first case study to implement the multivariate Bayesian predictive approach to the process optimization for the industrial application and its corresponding verification at two different production scales. This approach can be extended to other fermentation process optimizations and reliable operating region quantitation.

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The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016.