Establishing Rationale for the Clinical Development of Cell Therapy Products: Consensus between Risk and Benefit

Despite long-term research achievements, the development of cell therapy (CT) products remains challenging. This is because the risks experienced by the subject and therapeutic effects in the clinical trial stage are unclear due to the various uncertainties of CT when administered to humans. Nevertheless, as autologous cell products for systemic administration have recently been approved for marketing, CT product development is accelerating, particularly in the field of unmet medical needs. The human experience of CT remains insufficient compared with other classes of pharmaceuticals, while there are countless products for clinical development. Therefore, for many sponsors, understanding the rationale of human application of an investigational product based on the consensus and improving the ability to apply it appropriately for CT are necessary. Thus, defining the level of evidence for safety and efficacy fundamentally required for initiating the clinical development and preparing it using a reliable method for CT. Furthermore, the expertise should be strengthened in the design of the first-in-human trial, such as the starting dose and dose-escalation plan, based on a sufficiently acceptable rationale. Cultivating development professionals with these skills will increase the opportunity for more candidates to enter the clinical development phase.

Prediction of metabolizing enzymemediated clinical drug interactions using in vitro information

Evaluation of drug interactions is an essential step in the new drug development process.Regulatory agencies, including U.S. Food and Drug Administrations and European Medicines Agency, have been published documents containing guidelines to evaluate potential drug interactions. Here, we have streamlined in vitro experiments to assess metabolizing enzymemediated drug interactions and provided an overview of the overall process to evaluate potential clinical drug interactions using v data. An experimental approach is presented when an investigational drug (ID) is either a victim or a perpetrator, respectively, and the general procedure to obtain in vitro drug interaction parameters is also described. With the in vitro inhibitory and/or inductive parameters of the ID, basic, static, and/or dynamic models were used to evaluate potential clinical drug interactions. In addition to basic and static models which assume the most conservative conditions, such as the concentration of perpetrators as C max , dynamic models including physiologically-based pharmacokinetic models take into account changes in in vivo concentrations and metabolizing enzyme levels over time.

Predicting human pharmacokinetics from preclinical data: clearance

We have streamlined known in vitro methods used to predict the clearance (CL) of small molecules in humans in this tutorial. There have been many publications on in vitro methods that are used at different steps of human CL prediction. The steps from initial intrinsic CL measurement in vitro to the final application of the well-stirred model to obtain predicted hepatic CL (CLH ) are somewhat complicated. Except for the experts on drug metabolism and PBPK, many drug development scientists found it hard to figure out the entire picture of human CL prediction. To help readers overcome this barrier, we introduce each method briefly and demonstrate its usage in the chain of related equations destined to the CLH . Despite efforts in the laboratory steps, huge in vitro (predicted CLH )-in vivo (observed CLH ) discrepancy is not rare. A simple remedy to this discrepancy is to correct human predicted CLH using the ratio of in vitro-in vivo CLH obtained from animal species.

An experience on the model-based evaluation of pharmacokinetic drug-drug interaction for a long half-life drug

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and modelbased analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration.Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.

Predicting human pharmacokinetics from preclinical data: absorption

Predicting the rate and extent of oral absorption of drugs in humans has been a challenging task for new drug researchers. This tutorial reviews in vivo and PBPK methods reported in the past decades that are widely applied to predicting oral absorption in humans. The physicochemical property and permeability (typically obtained using Caco-2 system) data is the first necessity to predict the extent of absorption from the gut lumen to the intestinal epithelium (Fa). Intrinsic clearance measured using the human microsome or hepatocytes is also needed to predict the gut (Fg) and hepatic (Fh ) bioavailability. However, there are many issues with the correction of the inter-laboratory variability, hepatic cell membrane permeability, CYP3A4 dependency, etc. The bioavailability is finally calculated as F = F h × Fg × Fh . Although the rate of absorption differs by micro-environments and locations in the intestine, it may be simply represented by ka . The ka , the first-order absorption rate constant, is predicted from in vitro and in vivo data. However, human PK-predicting software based on these PBPK theories should be carefully used because there are many assumptions and variances. They include differences in laboratory methods, inter-laboratory variances, and theories behind the methods. Thus, the user's knowledge and experiences in PBPK and in vitro methods are necessary for proper human PK prediction.

Predicting human pharmacokinetics from preclinical data: volume of distribution

This tutorial introduces background and methods to predict the human volume of distribution (Vd ) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp . When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd . Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.g., Vc , Vp , and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd is huge, physiological plausibility of all input and output data (e.g., r2 -value of the allometric curve) may be reviewed for careful decision making.

Predicting human pharmacokinetics from preclinical data: absorption

Predicting the rate and extent of oral absorption of drugs in humans has been a challenging task for new drug researchers. This tutorial reviews in vivo and PBPK methods reported in the past decades that are widely applied to predicting oral absorption in humans. The physicochemical property and permeability (typically obtained using Caco-2 system) data is the first necessity to predict the extent of absorption from the gut lumen to the intestinal epithelium (Fa). Intrinsic clearance measured using the human microsome or hepatocytes is also needed to predict the gut (Fg) and hepatic (Fh ) bioavailability. However, there are many issues with the correction of the inter-laboratory variability, hepatic cell membrane permeability, CYP3A4 dependency, etc. The bioavailability is finally calculated as F = F h × Fg × Fh . Although the rate of absorption differs by micro-environments and locations in the intestine, it may be simply represented by ka . The ka , the first-order absorption rate constant, is predicted from in vitro and in vivo data. However, human PK-predicting software based on these PBPK theories should be carefully used because there are many assumptions and variances. They include differences in laboratory methods, inter-laboratory variances, and theories behind the methods. Thus, the user's knowledge and experiences in PBPK and in vitro methods are necessary for proper human PK prediction.

Predicting human pharmacokinetics from preclinical data: volume of distribution

This tutorial introduces background and methods to predict the human volume of distribution (Vd ) of drugs using in vitro and animal pharmacokinetic (PK) parameters. The physiologically based PK (PBPK) method is based on the familiar equation: Vd = Vp + ∑T (VT × ktp ). In this equation, Vp (plasma volume) and VT (tissue volume) are known physiological values, and ktp (tissue plasma partition coefficient) is experimentally measured. Here, the ktp may be predicted by PBPK models because it is known to be correlated with the physicochemical property of drugs and tissue composition (fraction of lipid and water). Thus, PBPK models' evolution to predict human Vd has been the efforts to find a better function giving a more accurate ktp . When animal PK parameters estimated using i.v. PK data in ≥ 3 species are available, allometric methods can also be used to predict human Vd . Unlike the PBPK method, many different models may be compared to find the best-fitting one in the allometry, a kind of empirical approach. Also, compartmental Vd parameters (e.g., Vc , Vp , and Q) can be predicted in the allometry. Although PBPK and allometric methods have long been used to predict Vd, there is no consensus on method choice. When the discrepancy between PBPK-predicted Vd and allometry-predicted Vd is huge, physiological plausibility of all input and output data (e.g., r2 -value of the allometric curve) may be reviewed for careful decision making.

Erratum: Population Pharmacokinetic Analysis of Metformin Administered as Fixed-Dose Combination in Korean Healthy Adults

In the published version of this article, the contents of Table 1 (‘Demographic characteristics of subjects’) are incorrect.

Population pharmacokinetic analysis of metformin administered as fixed-dose combination in Korean healthy adults

Metformin, an oral antihyperglycemic agent, is widely used as the first-line pharmacotherapy for type 2 diabetes mellitus (T2DM). It has been in use for several decades as numerous different formulations. However, despite its use, population pharmacokinetic (PK) modeling of metformin is not well developed. The aim of the present study was to evaluate the effect of formulation on PK parameters by developing a population PK model of metformin in Koreans and using this model to assess bioequivalence. We used a comparative PK study of a single agent and a fixed-dose combination of metformin in 36 healthy volunteers. The population PK model of metformin was developed using NONMEM (version 7.3). Visual predictive checks and bootstrap methods were performed to determine the adequacy of the model. The plasma concentration-time profile was best described by a two-compartment, first-order elimination model with first-order absorption followed by zeroorder absorption with lag time. From the covariate analysis, formulation had significant effect (p < 0.01) on relative bioavailability (F = 0.94) and first-order absorption constant (Ka = 0.83), but the difference was within the range of bioequivalence criteria. No other covariate was shown to have significant effect on PK parameters. The PK profile of the disposition phase was consistent with the published literature. However, in the present study, the multiple peaks found during the absorption phase implied the possible diversity of absorption PK profile depending on formulation or population. Unlike traditional bioequivalence analysis, the population PK model reflects formulation differences on specific parameters and reflected simulation can be performed.