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.

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.

Potency and plasma protein binding of drugs in vitro—a potentially misleading pair for predicting in vivo efficacious concentrations in humans

In drug discovery or preclinical stages of development, potency parameters such as IC₅₀, K(i), or K(d) in vitro have been routinely used to predict the parameters of efficacious exposure (AUC, C(min), etc.) in humans. However, to our knowledge, the fundamental assumption that the potency in vitro is correlated with the efficacious concentration in vivo in humans has not been investigated extensively. Thus, the present review examined this assumption by comparing a wide range of published pharmacokinetic (PK) and potency data. If the drug potency in vitro and its in vivo effectiveness in humans are well correlated, the steady-state average unbound concentrations in humans [C(u_ss.avg) = f(u)·F·Dose/(CL·τ) = f(u)·AUCss/τ] after treatment with approved dosage regimens should be higher than, or at least comparable to, the potency parameters assessed in vitro. We reviewed the ratios of C(u_ss.avg)/potency in vitro for a total of 54 drug entities (13 major therapeutic classes) using the dosage, PK, and in vitro potency reported in the published literature. For 54 drugs, the C(u_ss.avg)/in vitro potency ratios were < 1 for 38 (69%) and < 0.1 for 22 (34%) drugs. When the ratios were plotted against f(u) (unbound fraction), “ratio < 1” was predominant for drugs with high protein binding (90% of drugs with f(u) ≤ 5%; i.e., 28 of 31 drugs). Thus, predicting the in vivo efficacious unbound concentrations in humans using only in vitro potency data and f(u) should be avoided, especially for molecules with high protein binding.

Five years of the CiPA project (2013–2018): what did we learn?

Cases of drug-induced QT prolongation and sudden cardiac deaths resulted in market withdrawal of many drugs and world-wide regulatory changes through accepting the ICH guidelines E14 and S7B. However, because the guidelines were not comprehensive enough to cover the electrophysiological changes by drug-induced cardiac ion channel blocking, CiPA was initiated by experts in governments and academia in the USA, Europe, and Japan in 2013. Five years have passed since the launch of the CiPA initiative that aimed to improve the current ICH guidelines. This report reviews the current achievements of the CiPA initiative and explores unresolved issues.

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.

Erratum: R-based reproduction of the estimation process hidden behind NONMEM Part 2: First-order conditional estimation

The equations on page 162 should be corrected.

Physiologically-based pharmacokinetic predictions of intestinal BCRP-mediated drug interactions of rosuvastatin in Koreans

It was recently reported that the C(max) and AUC of rosuvastatin increases when it is coadministered with telmisartan and cyclosporine. Rosuvastatin is known to be a substrate of OATP1B1, OATP1B3, NTCP, and BCRP transporters. The aim of this study was to explore the mechanism of the interactions between rosuvastatin and two perpetrators, telmisartan and cyclosporine. Published (cyclosporine) or newly developed (telmisartan) PBPK models were used to this end. The rosuvastatin model in Simcyp (version 15)'s drug library was modified to reflect racial differences in rosuvastatin exposure. In the telmisartan–rosuvastatin case, simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without telmisartan) ratios were 1.92 and 1.14, respectively, and the T(max) changed from 3.35 h to 1.40 h with coadministration of telmisartan, which were consistent with the aforementioned report (C(maxI)/C(max): 2.01, AUCI/AUC:1.18, T(max): 5 h → 0.75 h). In the next case of cyclosporine–rosuvastatin, the simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without cyclosporine) ratios were 3.29 and 1.30, respectively. The decrease in the CL(int,BCRP,intestine) of rosuvastatin by telmisartan and cyclosporine in the PBPK model was pivotal to reproducing this finding in Simcyp. Our PBPK model demonstrated that the major causes of increase in rosuvastatin exposure are mediated by intestinal BCRP (rosuvastatin–telmisartan interaction) or by both of BCRP and OATP1B1/3 (rosuvastatin–cyclosporine interaction).

Principles of transparency and clinical trial registration

No abstract available.

Development and validation of analytical method for the determination of radotinib in human plasma using liquid chromatography-tandem mass spectrometry

This study describes the development of an analytical method to determine radotinib levels in human plasma using high performance liquid chromatography (HPLC) coupled with triple quadrupole tandem mass spectrometry (MS/MS) for pharmacokinetic application. Plasma samples were sequentially processed by liquid-liquid extraction using methyl tert-butyl ether, evaporation, and reconstitution. Analytes were separated and analyzed using HPLC-MS/MS in selected reaction monitoring mode, monitoring the specific transitions of m/z 531 to 290 for radotinib and m/z 409 to 238 for amlodipine (internal standard). The HPLC-MS/MS analytical method was validated with respect to selectivity, linearity, sensitivity, accuracy, precision, recovery, and stability. Calibration curves were linear over a concentration range 5–3,000 ng/mL with correlation coefficients (r) > 0.998. The lower limit of quantification for radotinib in plasma was 5 ng/mL. The accuracy and precision of the analytical method were acceptable within 15% at all quality control levels. This method was suitable to determine radotinib levels in human plasma because of its simplicity, selectivity, precision, and accuracy.

Tutorial for beginners: the concept of clearance explained using the example of a vacuum cleaner

Clearance is a key concept in pharmacokinetics, but it is not easy to understand for beginners. This tutorial aims to help beginners by using the analogy of a vacuum cleaner clearing the dust from the air in a room. The air, the volume of the air, the dust and the vacuum cleaner are used to represent the plasma, the volume of distribution, the drug and the eliminating organ, respectively, in the human body. Because the capacity of a vacuum cleaner (eliminating organ) is an inherent feature that is independent of the concentration of dust (drug), the elimination rate (eliminated amount/time) of dust (drug), which is proportional to its concentration in the air (plasma), cannot reflect this inherent capacity correctly. Clearance estimates the volume of the solvent (air or plasma) cleared by the organ per unit time rather than the amount of the solute (dust or drug) removed. Therefore, clearance has the unit of volume/time. Just as the air is cleared of dust, but is not eliminated by the vacuum cleaner, the plasma is cleared of drug, but is not eliminated from the human body.

Hydrochlorothiazide does not increase furosemide's effects in end-stage renal disease

Diuretic therapy for the treatment of edema in patients with end-stage renal disease (ESRD) is unsatisfactory, and a combination of thiazide and loop diuretics may produce better clinical effects. To evaluate the influence of thiazide on loop diuretic therapy for ESRD, we performed a crossover study of furosemide versus hydrochlorothiazide plus furosemide treatment. The diuretic effects of furosemide (160 mg i.v.) alone versus a combination of hydrochlorothiazide (100 mg p.o.) and furosemide were studied in ten ESRD patients with proteinuria greater than 1 g/day. The diuretic effects were compared for 24 h urine volume and electrolyte excretion. To detect the influence of thiazide that may have been obscured in the widely dispersed data, pharmacodynamic analysis of urine furosemide excretion rate versus fractional excretion of sodium (FeNa) was also performed using mixed-effect modeling. Combination therapy was not significantly different from furosemide monotherapy in terms of 24 h urine volume, chloride, or sodium excretion. Hydrochlorothiazide was not a significant covariate in the furosemide effect for the pharmacodynamic model. In patients with ESRD and severe proteinuria (>1,000 mg/day), the combination of hydrochlorothiazide with furosemide therapy did not increase the diuretic effect of furosemide.

Development of an automated appendix generation system (ARGUS) for clinical study reports

Data handling and tabulation are a time-consuming job when writing appendices for clinical study reports. The authors have developed an automated appendix generation system (ARGUS) conforming to the CDISC/SDTM standard using SAS (version 9.3) and R (version 3.3.1: for PK plot generation). It consists of the one main program and three subprograms. The program runs to convert a database file into an appendix document with about 100 tables and plots in MS Word format within one min after pressing the submit button under common desktop environments. We found that tasks of constructing appendices for a typical 2×2 crossover design study that have taken our team about 8 days were completed within 6 or 7 hours using the ARGUS system.

Decreased potency of fimasartan in liver cirrhosis was quantified using mixed-effects analysis

Fimasartan is a nonpeptide angiotensin II receptor blocker. In a previous study that compared the pharmacokinetics (PK) of fimasartan between patients with hepatic impairment (cirrhosis) and healthy subjects, the exposure to fimasartan was found to be higher in patients, but the decrease of blood pressure (BP) was not clinically significant in those with moderate hepatic impairment. The aims of this study were to develop a population PK-pharmacodynamic (PD) model of fimasartan and to evaluate the effect of hepatic function on BP reduction by fimasartan using previously published data. A 2-compartment linear model with mixed zero-order absorption followed by first-order absorption with a lag time adequately described fimasartan PK, and the effect of fimasartan on BP changes was well explained by the inhibitory sigmoid function in the turnover PK-PD model overlaid with a model of circadian rhythm (NONMEM version 7.2). According to our PD model, the lower BP responses in hepatic impairment were the result of the increased fimasartan EC₅₀ in patients, rather than from a saturation of effect. This is congruent with the reported pathophysiological change of increased plasma ACE and renin activity in hepatic cirrhosis.

Revisiting the well-stirred model of hepatic clearance: Q(H), CL(H) and F changing in the same direction

This tutorial examines the relationship between CL, F, and hepatic blood flow (Q(H)) quantitatively at oral and i.v. administration as an answer to the quiz set for KSCPT members. In case of oral dosing, when hepatic blood flow increases, the hepatic clearance (CL) and bioavailability (F) increases in high-extraction ratio drugs according to the well-stirred model equations for hepatic clearance: CL(H) = Q(H)·ER = Q(H)·f(u)·CL(int)/(Q(H)+f(u)·CL(int)) and F = 1 - ER Despite such a clear relationship, many students may feel it rather paradoxical that the increased CL (thus decreasing the AUC) causes increased F and thus the AUC (F·Dose/CLH) remains unchanged. This tutorial clarifies that the degree to which CL increase fails to match that of the Q(H) increase, and thus the decreased ER (= CL/Q(H)) that results in the increased F. Contemplating this simple, but seemingly paradoxical phenomenon may help students gain a deeper understanding of the first-pass effect.

Tips for the choice of initial estimates in NONMEM

The importance of precise information and knowledge on the initial estimates (IEs) in modeling has not been paid its due attention so far. By focusing on the IE, this tutorial may serve as a practical guide for beginners in pharmacometrics. A 'good' set of IEs rather than arbitrary values is required because the IEs where NONMEM kicks off its estimation may influence the subsequent objective function minimization. To provide NONMEM with acceptable IEs, modelers should understand the exact meaning of THETA, OMEGA and SIGMA based on physiology. In practice, problems related to the value of the IE are more likely to occur for THETAs rather than the random-effect terms. Because it is almost impossible for a modeler to give a precise IE for OMEGAs at the beginning, it may be a good practice to start at relatively small IEs for them. NONMEM may fail to converge when too small IEs are provided for residual error parameters; thus, it is recommended to give sufficiently large values for them. The understandings on the roles, meanings and implications of IEs even help modelers in troubleshooting situations which frequently occur over the whole modeling process.

Erratum: Clearance

The third equation on page 44 should be corrected.