The Use of In Vitro Data and Physiologically-Based Pharmacokinetic Modeling to Predict Drug Metabolite Exposure: Desipramine Exposure in Cytochrome P4502D6 Extensive and Poor Metabolizers Following Administration of Imipramine

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Ca2+-Activated K+ Channels in Progenitor Cells of Musculoskeletal Tissues: A Narrative Review

Musculoskeletal disorders represent one of the main causes of disability worldwide, and their prevalence is predicted to increase in the coming decades. Stem cell therapy may be a promising option for the treatment of some of the musculoskeletal diseases. Although significant progress has been made in musculoskeletal stem cell research, osteoarthritis, the most-common musculoskeletal disorder, still lacks curative treatment. To fine-tune stem-cell-based therapy, it is necessary to focus on the underlying biological mechanisms. Ion channels and the bioelectric signals they generate control the proliferation, differentiation, and migration of musculoskeletal progenitor cells. Calcium- and voltage-activated potassium (KCa) channels are key players in cell physiology in cells of the musculoskeletal system. This review article focused on the big conductance (BK) KCa channels. The regulatory function of BK channels requires interactions with diverse sets of proteins that have different functions in tissue-resident stem cells. In this narrative review article, we discuss the main ion channels of musculoskeletal stem cells, with a focus on calcium-dependent potassium channels, especially on the large conductance BK channel. We review their expression and function in progenitor cell proliferation, differentiation, and migration and highlight gaps in current knowledge on their involvement in musculoskeletal diseases.

Variability in Human In Vitro Enzyme Kinetics

There are many factors which are known to cause variability in human in vitro enzyme kinetic data. Factors such as the source of enzyme and how it was prepared, the genetics and background of the donor, how the in vitro studies are designed, and how the data are analyzed contribute to variability in the resulting kinetic parameters. It is important to consider not only the factors which cause variability within an experiment, such as selection of a probe substrate, but also those that cause variability when comparing kinetic data across studies and laboratories. For example, the artificial nature of the microsomal lipid membrane and microenvironment in some recombinantly expressed enzymes, relative to those found in native tissue microsomes, has been shown to influence enzyme activity and thus can be a source of variability when comparing across the two different systems. All of these factors, and several others, are discussed in detail in the chapter below. In addition, approaches which can be used to visualize the uncertainty arising from the use of enzyme kinetic data within the context of predicting human pharmacokinetics are discussed.

Re-Examining the Impact of Minimal Scans in Liquid Chromatography-Mass Spectrometry Analysis

Liquid chromatography-mass spectrometry (LC-MS) is one of the most widely used analytical tools. High analysis volumes and sample complexity often demand more informative LC-MS acquisition schemes to improve efficiency and throughput without compromising data quality, and such a demand has been always hindered by the prerequisite that a minimum of 13-20 MS scans (data points) across an analyte peak are required for accurate quantitation. The current study systematically re-evaluated and compared the impact of different scan numbers on quantitation analysis using both triple quadrupoles mass spectrometry (TQMS) and high-resolution mass spectrometry (HRMS). Contrary to the 13-20 minimal scan prerequisite, the data obtained from a group of eight commercial drugs in the absence and presence of biological matrices suggest that 6 scans per analyte peak are sufficient to achieve highly comparable quantitation results compared to that obtained using 10 and 20 scans, respectively. The fewer minimal scan prerequisite is presumably attributed to an improved LC system and advanced column technology, better MS detector, and more intelligent peak detection and integration algorithms leading to a more symmetric peak shape and smaller peak standard deviation. As a result, more informative acquisition schemes can be broadly set up for higher throughput and more data-rich LC-MS/MS analysis as demonstrated in a hepatocyte clearance assay in which fewer MS scans executed on HRMS led to broader metabolite coverage without compromising data quality in hepatic clearance assessment. The demonstrated acquisition scheme would substantially increase the throughput, robustness, and richness of the nonregulatory analysis, which can be broadly applied in diverse fields including pharmaceutical, environmental, forensic, toxicological, and biotechnological.

Pharmacokinetics-On-a-Chip: In Vitro Microphysiological Models for Emulating of Drugs ADME

Despite many ongoing efforts across the full spectrum of pharmaceutical and biotech industries, drug development is still a costly undertaking that involves a high risk of failure during clinical trials. Animal models played vital roles in understanding the mechanism of human diseases. However, the use of these models has been a subject of heated debate, particularly due to ethical matters and the inevitable pathophysiological differences between animals and humans. Current in vitro models lack the sufficient functionality and predictivity of human pharmacokinetics and toxicity, therefore, are not capable to fully replace animal models. The recent development of micro-physiological systems has shown great potential as indispensable tools for recapitulating key physiological parameters of humans and providing in vitro methods for predicting the pharmacokinetics and pharmacodynamics in humans. Integration of Absorption, Distribution, Metabolism, and Excretion (ADME) processes within one close in vitro system is a paramount development that would meet important unmet pharmaceutical industry needs. In this review paper, synthesis of the ADME-centered organ-on-a-chip technology is systemically presented from what is achieved to what needs to be done, emphasizing the requirements of in vitro models that meet industrial needs in terms of the structure and functions.

Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL_int ) and fractional contribution of the metabolizing enzyme toward total metabolism (f_m ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL_int and f_m , 29 and 20 new drug applications were included for evaluation, respectively. For CL_int , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f_m , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL_int were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f_m from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f_m when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL_int and f_m still need to be optimized based on in vivo data.

Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies

3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CL_int) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CL_int within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.

Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model

In the development of new drugs, the prediction of metabolite‐to‐parent plasma exposure ratio in humans prior to administration in a clinical study has emerged as an important need. In this work, we derived a mechanistic static model based on first principles to estimate metabolite‐to‐parent plasma exposure ratio, considering the contribution of liver and gut metabolism and drug transport. Knowledge (or assumptions) of mechanisms of clearance and organs involved is required. Input parameters needed included intrinsic clearance, fraction of clearance to the metabolite of interest, various binding values, and, in some cases, active transport clearance. The principles are illustrated with four drugs that yield six metabolites, with one in which clearance is dependent on a pathway subject to genetic polymorphism. Overall, the approach yielded metabolite‐to‐parent ratios within about twofold of the actual values and, thus, can be valuable in decision making in the drug development process.

Does obtaining CYP2D6 and CYP2C19 pharmacogenetic testing predict antidepressant response or adverse drug reactions?

Treatment non-response and adverse reactions are common in patients receiving antidepressants. Personalizing psychiatric treatment based on pharmacogenetic testing has been proposed to help clinicians guide antidepressant selection and dosing. This systematic literature review assesses the two most robustly studied drug-metabolizing enzymes, CYP2D6 and CYP2C19, and examines whether obtaining CYP2D6 and CYP2C19 testing can be used to predict antidepressant response or adverse drug reactions in order to improve clinical outcomes. In general, literature reviews published prior to 2013 indicated that results have been inconsistent linking CYP2D6 and CYP2C19 to antidepressant treatment outcomes, suggesting that more evidence is required to support the clinical implementation of genotyping to predict outcomes. We thus performed an extensive and systematic literature review, focusing on studies published from 2013 through 2018. Sixteen studies were found to be relevant. The results yielded inconsistent findings, suggesting that CYP2D6 and CYP2C19 testing may predict response in certain individuals, but it remains unclear if this will translate to improved clinical outcomes. Further research is required to determine when pharmacogenetic testing should be utilized and in which populations it is indicated. Randomized, controlled, prospective trials with adequate sample sizes would best clarify whether genotype-guided antidepressant selection will ultimately improve clinical outcomes.

Pharmacokinetics, Tissue Distribution and Excretion of a Novel Diuretic (PU-48) in Rats

Methyl 3-amino-6-methoxythieno [2,3-b] quinoline-2-carboxylate (PU-48) is a novel diuretic urea transporter inhibitor. The aim of this study is to investigate the profile of plasma pharmacokinetics, tissue distribution, and excretion by oral dosing of PU-48 in rats. Concentrations of PU-48 within biological samples are determined using a validated high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. After oral administration of PU-48 (3, 6, and 12 mg/kg, respectively) in self-nanomicroemulsifying drug delivery system (SNEDDS) formulation, the peak plasma concentrations (C_max), and the area under the curve (AUC_0⁻∞) were increased by the dose-dependent and linear manner, but the marked different of plasma half-life (t_1/2) were not observed. This suggests that the pharmacokinetic profile of PU-48 prototype was first-order elimination kinetic characteristics within the oral three doses range in rat plasma. Moreover, the prototype of PU-48 was rapidly and extensively distributed into thirteen tissues, especially higher concentrations were detected in stomach, intestine, liver, kidney, and bladder. The total accumulative excretion of PU-48 in the urine, feces, and bile was less than 2%. This research is the first report on disposition via oral administration of PU-48 in rats, and it provides important information for further development of PU-48 as a diuretic drug candidate.

An Investigation into the Prediction of the Plasma Concentration-Time Profile and Its Interindividual Variability for a Range of Flavin-Containing Monooxygenase Substrates Using a Physiologically Based Pharmacokinetic Modeling Approach

Our recent paper demonstrated the ability to predict in vivo clearance of flavin-containing monooxygenase (FMO) drug substrates using in vitro human hepatocyte and human liver microsomal intrinsic clearance with standard scaling approaches. In this paper, we apply a physiologically based pharmacokinetic (PBPK) modeling and simulation approach (M&S) to predict the clearance, area under the curve (AUC), and C_max values together with the plasma profile of a range of drugs from the original study. The human physiologic parameters for FMO, such as enzyme abundance in liver, kidney, and gut, were derived from in vitro data and clinical pharmacogenetics studies. The drugs investigated include itopride, benzydamine, tozasertib, tamoxifen, moclobemide, imipramine, clozapine, ranitidine, and olanzapine. The fraction metabolized by FMO for these drugs ranged from 21% to 96%. The developed PBPK models were verified with data from multiple clinical studies. An attempt was made to estimate the scaling factor for recombinant FMO (rFMO) using a parameter estimation approach and automated sensitivity analysis within the PBPK platform. Simulated oral clearance using in vitro hepatocyte data and associated extrahepatic FMO data predicts the observed in vivo plasma concentration profile reasonably well and predicts the AUC for all of the FMO substrates within 2-fold of the observed clinical data; seven of the nine compounds fell within 2-fold when human liver microsomal data were used. rFMO overpredicted the AUC by approximately 2.5-fold for three of the nine compounds. Applying a calculated intersystem extrapolation scalar or tissue-specific scalar for the rFMO data resulted in better prediction of clinical data. The PBPK M&S results from this study demonstrate that human hepatocytes and human liver microsomes can be used along with our standard scaling approaches to predict human in vivo pharmacokinetic parameters for FMO substrates.

Estimation of Circulating Drug Metabolite Exposure in Human Using In Vitro Data and Physiologically Based Pharmacokinetic Modeling: Example of a High Metabolite/Parent Drug Ratio

(R)-4-((4-(((4-((tetrahydrofuran-3-yl)oxy)benzo[d]isoxazol-3-yl)oxy)methyl)piperidin-1-yl)methyl)tetrahydro-2H-pyran-4-ol (TBPT), a serotonin-4 receptor partial agonist, is metabolized to two metabolites: an N-dealkylation product [(R)-3-(piperidin-4-ylmethoxy)-4-((tetrahydrofuran-3-yl)oxy)benzo[d]isoxazole (M1)] and a cyclized oxazolidine structure [7-(((4-(((R)-tetrahydrofuran-3-yl)oxy)benzo[d]isoxazol-3-yl)oxy)methyl)octahydro-3H (M2)]. After administration of TBPT to humans the exposure to M1 was low and the exposure to M2 was high, relative to the parent drug, despite this being the opposite in vitro. In this study, projection of the plasma metabolite/parent (M/P) ratios for M1 and M2 was attempted using in vitro metabolism, binding, and permeability data in static and dynamic physiologically based pharmacokinetic (PBPK) models. In the static model, the fraction of parent clearance yielding the metabolite (which also required taking into account secondary metabolites of M1 and M2), the clearance of the metabolites and parent, and an estimate of the availability of the metabolites from the liver were combined to yield estimated parent/metabolite ratios of 0.32 and 23 for M1 and M2, respectively. PBPK modeling that used in vitro and physicochemical data input yielded estimates of 0.26 and 20, respectively. The actual values were 0.12 for M1/TBPT and 58 for M2/TBPT. Thus, the ratio for M1 was overpredicted, albeit at values less than unity. The ratio for M2/TBPT was underpredicted, and the high ratio of 58 may exceed a limiting ceiling of the approach. Nevertheless, when considered in the context of determining whether a potential circulating metabolite may be quantitatively important prior to administration of a drug for the first time to humans, the approaches succeeded in highlighting the importance of M2 (M/P ratio >> 1) relative to M1, despite M1 being much greater than M2 in vitro.

Metabolites of alectinib in human: their identification and pharmacological activity

Two metabolites (M4 and M1b) in plasma and four metabolites (M4, M6, M1a and M1b) in faeces were detected through the human ADME study following a single oral administration of [¹⁴C]alectinib, a small-molecule anaplastic lymphoma kinase inhibitor, to healthy subjects. In the present study, M1a and M1b, which chemical structures had not been identified prior to the human ADME study, were identified as isomers of a carboxylate metabolite oxidatively cleaved at the morpholine ring. In faeces, M4 and M1b were the main metabolites, which shows that the biotransformation to M4 and M1b represents two main metabolic pathways for alectinib. In plasma, M4 was a major metabolite and M1b was a minor metabolite. The contribution to in vivo pharmacological activity of these circulating metabolites was assessed from their in vitro pharmacological activity and plasma protein binding. M4 had a similar cancer cell growth inhibitory activity and plasma protein binding to that of alectinib, suggesting its contribution to the antitumor activity of alectinib, whereas the pharmacological activity of M1b was insignificant.