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Physiologically-based pharmacokinetic (PBPK) modeling is increasingly used to predict drug disposition and drug-drug interactions (DDIs). However, accurately predicting the pharmacokinetics of transporter substrates and transporter-mediated DDIs (tDDIs) is still challenging. Rosuvastatin is a commonly used substrate probe in DDI risk assessment for new molecular entities (NMEs) that are potential organic anion transporting polypeptide 1B or breast cancer resistance protein transporter inhibitors, and as such, several rosuvastatin PBPK models have been developed to try to predict the clinical DDI and support NME drug labeling. In this review, we examine five representative PBPK rosuvastatin models, discuss common challenges that the models have come across, and note remaining gaps. These shared learnings will help with the continuing efforts of rosuvastatin model validation, provide more information to understand transporter-mediated drug disposition, and increase confidence in tDDI prediction.

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.

Accurately predicting the pharmacokinetics of compounds that are transporter substrates has been notoriously challenging using traditional in vitro systems and physiologically based pharmacokinetic (PBPK) modeling. The objective of this study was to use PBPK modeling to understand the translational accuracy of data generated with human embryonic kidney 293 (HEK293) cells overexpressing the hepatic uptake transporters organic anion transporting polypeptide (OATP) 1B1/3 with and without plasma while accounting for transporter expression. Models of four OATP substrates, two with low protein binding (pravastatin and rosuvastatin) and two with high protein binding (repaglinide and pitavastatin) were explored, and the OATP in vitro data generated in plasma incubations were used for a plasma model, and in buffer incubations for a buffer model. The pharmacokinetic parameters and concentration-time profiles of pravastatin and rosuvastatin were similar and well predicted (within 2-fold of observed values) using the plasma and buffer models without needing an empirical scaling factor, whereas the dispositions of the highly protein bound repaglinide and pitavastatin were more accurately simulated with the plasma models than the buffer models. This work suggests that data from HEK293 overexpressing transporter cells corrected for transporter expression represent a valid approach to improve bottom-up PBPK modeling for highly protein bound OATP substrates with plasma incubations and low protein binding OATP substrates with or without plasma incubations. SIGNIFICANCE STATEMENT: This work demonstrates the bottom-up approach of using in vitro data directly without employing empirical scaling factors to predict the intravenous pharmacokinetic (PK) profiles reasonably well for four organic anion transporting polypeptide (OATP) substrates. Based on these results, using HEK293 overexpressing cells, examining the impact of plasma for highly bound compounds, and incorporating transporter quantitation for the lot in which the in vitro data were generated represents a valid approach to achieve more accurate prospective PK predictions for OATP substrates.

The non-receptor protein tyrosine phosphatase SHP2 (PTPN11) plays an important role in the regulation of RAS/MAPK signal transduction downstream of growth factor receptor activation. Loss of SHP2 activity suppresses tumor cell growth, making SHP2 a potential target for cancer therapy. Here we report the discovery of GDC-1971 (formerly RLY-1971), a highly potent, selective, and orally bioavailable small-molecule SHP2 inhibitor that stabilizes SHP2 in a closed, auto-inhibited conformation. GDC-1971 potently inhibits both wild-type SHP2 (IC50 1 uM), indicating a link between KRAS GTP hydrolysis and SHP2 dependency. Despite this trend, some non-KRAS G12C or G12A cell lines harboring other KRAS mutations responded to GDC-1971 in vitro, suggesting some heterogeneity in RTK/SHP2 signaling dependence in subsets of other KRAS mutants. In vivo, GDC-1971 demonstrates dose-dependent RAS/MAPK pathway inhibition and induces significant tumor-growth inhibition in human xenograft models harboring EGFR and KRAS alterations at continuous daily doses that are well tolerated. Given the reported role of SHP2 as a critical mediator of resistance to targeted therapies, we assessed the activity of GDC-1971 combinations in multiple contexts. We observed increased suppression of the MAPK signaling cascade and anti-proliferation synergy when combining GDC-1971 with EGFR, ALK, and KRAS G12C inhibitors in vitro. The observed in vitro synergy translated to dramatic anti-tumor growth effects in vivo. GDC-1971 in combination with the KRAS G12C covalent inhibitor GDC-6036 resulted in significant regressions at doses well below those required for single agent activity in a KRAS G12C-mutant NSCLC xenograft model. In rodent and dog toxicology studies, GDC-1971 is well tolerated at exposures above those required to induce regression in xenograft models. The biochemical and cellular potency and favorable pharmaceutical properties of GDC-1971 support the further clinical development in RTK/MAPK pathway altered tumors using continuous daily dosing alone and in combination with other targeted agents, including the KRAS-G12C inhibitor GDC-6036 (clinical trial NCT04449874). Citation Format: Bret Williams, Alexander Taylor, Olivia Orozco, Christopher Owen, Elizabeth Kelley, Andre Lescarbeau, Kelley Shortsleeves, Randy Kipp, Vy Nguyen, Erin Brophy, Jeremy Wilbur, Yong Tang, David Lanzetta, Nigel Waters, Sherri Smith, Fabrizio Giordanetto, Paul Maragakis, Jack Greismann, Lindsay Willmore, Eric Therrien, Yang Xiao, Marie Evangelista, Luca Gerosa, Eva Lin, Mark Merchant, Alfonso Arrazate, Emily Chan, Pablo Sáenz-López Larrocha, Stefan Chun, Thomas Hunsaker, Gauri Deshmukh, Christine M. Bowman, David E. Shaw, Mark Murcko, Mahesh Padval, W Patrick Walters, James Watters, Donald A. Bergstrom. Discovery and characterization of the potent, allosteric SHP2 inhibitor GDC-1971 for the treatment of RTK/RAS driven tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3327.

The emergence and continued evolution of the transporter field has caused re-evaluation and refinement of the original principles surrounding drug disposition. In this paper, we emphasize the impactthat transporters can have on volume of distribution and how this can affect the other major pharmacokinetic parameters. When metabolic drug-drug interactions or pharmacogenomic variance changes the metabolism of a drug, the volume of distribution appears to be unchanged while clearance, bioavailability, and half-life are changed. When transporters are involved in the drug-drug interactions or pharmacogenomic variance, the volume of distribution can be markedly affected causing counterintuitive changes in half-life. Cases are examined where a volume of distribution change is significant enough that although clearance decreases, half-life decreases. Thus, drug dosing decisions must be made based on CL/F changes, not half-life changes, as such volume of distribution alterations will also influence the half-life results.

Generating accurate in vitro data is crucial for in vitro to in vivo extrapolation and pharmacokinetic predictions. The use of human embryonic kidney (HEK) 293 cells overexpressing organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 in protein-free buffer and 100% human plasma incubations was explored for the uptake of four OATP substrates: pravastatin, rosuvastatin, repaglinide, and pitavastatin. Differences were observed for each parameter [unbound Michaelis constant (Km,u), Vmax, intrinsic clearance (CLint), and unbound passive diffusion Pdif,u] obtained from the buffer and plasma incubations in both cells, and the fold differences were greatest for the highly protein bound compounds. The fold change in Km,u values ranged from 1.91 to 619, and the fold change in Vmax values ranged from 1.22 to 97.4. As a result, in both cells, the CLint values generated in the plasma incubations were higher by 0.762- to 31.7-fold than the values generated in the protein-free buffer. The passive diffusion was also higher in the plasma incubations for all four compounds, with a fold difference range of 1.73–23.4. These shifts in the presence and absence of human plasma suggest that plasma proteins may play a role in both the active uptake and passive diffusion processes. The results also support the idea of a transporter-induced protein-binding shift, where high protein binding may not limit the uptake of compounds that have high affinity for transporters. The addition of plasma to incubations leading to higher CLint values for transporter substrates helps mitigate the underprediction commonly noted with in vitro to in vivo extrapolation. SIGNIFICANCE STATEMENT The current investigation brings a new perspective on how to mitigate the underprediction commonly noted with in vitro to in vivo extrapolation for OATP substrates by using HEK293 cells overexpressing OATP1B1 and OATP1B3. It also supports the idea of a transporter-induced protein-binding shift, where high protein binding may not limit the uptake of compounds that have high affinity for transporters.

When predicting hepatic clearance using in vitro to in vivo extrapolation (IVIVE), microsomes or hepatocytes are commonly used. Here, we examine intrinsic clearance values and IVIVE results in human hepatocytes and microsomes for compounds metabolized by a variety of enzymes. The great majority of CYP3A4 substrates examined had higher intrinsic clearance values in microsomes compared with hepatocytes, whereas the values were more similar between the two incubations for substrates of other enzymes. We hypothesize that this may be due to interplay between CYP3A4 and the efflux transporter P-glycoprotein, as they have been shown to exhibit coordinated regulation. When examining the prediction accuracy for substrates of other enzymes between microsomes and hepatocytes, average fold errors as well as overall error were similar, demonstrating once again that IVIVE methods are not adequately defined and understood. SIGNIFICANCE STATEMENT: For CYP3A4 substrates, microsomes give markedly higher predictive in vitro to in vivo extrapolation than for other metabolic enzymes, which is not found for hepatocytes. We hypothesize that this could be a result of CYP3A4-P-glycoprotein interplay or coordinated regulation in hepatocytes that would not be observed in microsomes.

Accurately predicting the pharmacokinetics of compounds that are transporter substrates has been notoriously challenging using traditional in vitro systems and physiologically based pharmacokinetic (PBPK) modeling. The objective of this study was to use PBPK modeling to understand the translational accuracy of data generated with human embryonic kidney 293 (HEK293) cells overexpressing the hepatic uptake transporters organic anion transporting polypeptide (OATP) 1B1/3 with and without plasma while accounting for transporter expression. Models of four OATP substrates, two with low protein binding (pravastatin and rosuvastatin) and two with high protein binding (repaglinide and pitavastatin) were explored, and the OATP in vitro data generated in plasma incubations were used for a plasma model, and in buffer incubations for a buffer model. The pharmacokinetic parameters and concentration-time profiles of pravastatin and rosuvastatin were similar and well predicted (within 2-fold of observed values) using the plasma and buffer models without needing an empirical scaling factor, whereas the dispositions of the highly protein bound repaglinide and pitavastatin were more accurately simulated with the plasma models than the buffer models. This work suggests that data from HEK293 overexpressing transporter cells corrected for transporter expression represent a valid approach to improve bottom-up PBPK modeling for highly protein bound OATP substrates with plasma incubations and low protein binding OATP substrates with or without plasma incubations. SIGNIFICANCE STATEMENT This work demonstrates the bottom-up approach of using in vitro data directly without employing empirical scaling factors to predict the intravenous pharmacokinetic (PK) profiles reasonably well for four organic anion transporting polypeptide (OATP) substrates. Based on these results, using HEK293 overexpressing cells, examining the impact of plasma for highly bound compounds, and incorporating transporter quantitation for the lot in which the in vitro data were generated represents a valid approach to achieve more accurate prospective PK predictions for OATP substrates.

Here we characterize and summarize the pharmacokinetic changes for metabolized drugs when drug-drug interactions and pharmacogenomic variance are observed. Following multiple dosing to steady-state, oral systemic concentration-time curves appear to follow a one-compartment body model, with a shorter rate limiting half-life, often significantly shorter than the single dose terminal half-life. This simplified disposition model at steady-state allows comparisons of measurable parameters (i.e., area under the curve, half-life, maximum concentration and time to maximum concentration) following drug interaction or pharmacogenomic variant studies to be utilized to characterize whether a drug is low versus high hepatic extraction ratio, even without intravenous dosing. The characteristics of drugs based on the ratios of area under the curve, maximum concentration and half-life are identified with recognition that volume of distribution is essentially unchanged for drug interaction and pharmacogenomic variant studies where only metabolic outcomes are changed and transporters are not significantly involved. Comparison of maximum concentration changes following single dose interaction and pharmacogenomic variance studies may also identify the significance of intestinal first pass changes. The irrelevance of protein binding changes on pharmacodynamic outcomes following oral and intravenous dosing of low hepatic extraction ratio drugs, versus its relevance for high hepatic extraction ratio drugs is re-emphasized.

PURPOSE: To examine the interlaboratory variability in CL(int) values generated with human hepatocytes and determine trends in variability and clearance prediction accuracy using physicochemical and pharmacokinetic parameters. METHODS: Data for 50 compounds from 14 papers were compiled with physicochemical and pharmacokinetic parameter values taken from various sources. RESULTS: Coefficients of variation were as high as 99.8% for individual compounds and variation was not dependent on the number of prediction values included in the analysis. When examining median values, it appeared that compounds with a lower number of rotatable bonds had more variability. When examining prediction uniformity, those compounds with uniform in vivo underpredictions had higher CL(int,) in vivo values, while those with non-uniform predictions typically had lower CL(int,) in vivo values. Of the compounds with uniform predictions, only a small number were uniformly predicted accurately. Based on this limited dataset, less lipophilic, lower intrinsic clearance, and lower protein binding compounds yield more accurate clearance predictions. CONCLUSIONS: Caution should be taken when compiling in vitro CL(int) values from different laboratories as variations in experimental procedures (such as extent of shaking during incubation) may yield different predictions for the same compound. The majority of compounds with uniform in vitro values had predictions that were inaccurate, emphasizing the need for a better mechanistic understanding of IVIVE. The non-uniform predictions, often with low turnover compounds, reaffirmed the experimental challenges for drugs in this clearance range. Separating new chemical entities by lipophilicity, intrinsic clearance, and protein binding may help instill more confidence in IVIVE predictions.

Accurately predicting hepatic clearance is an integral part of the drug-development process, and yet current in vitro to in vivo (IVIVE) extrapolation methods yield poor predictions, particularly for highly protein-bound transporter substrates. Explanations for error include inaccuracies in protein-binding measurements and the lack of recognition of protein-facilitated uptake, where both unbound and bound drug may be cleared, violating the principles of the widely accepted free drug theory. A new explanation for protein-facilitated uptake is proposed here, called a transporter-induced protein binding shift. High-affinity binding to cell-membrane proteins may change the equilibrium of the nonspecific binding between drugs and plasma proteins, leading to greater cellular uptake and clearance than currently predicted. The uptake of two lower protein-binding organic anion transporting polypeptide substrates (pravastatin and rosuvastatin) and two higher binding substrates (atorvastatin and pitavastatin) were measured in rat hepatocytes in incubations with protein-free buffer versus 100% plasma. Decreased unbound K(m) values and increased intrinsic clearance values were seen in the plasma incubations for the highly bound compounds, supporting the new hypothesis and mitigating the IVIVE underprediction previously seen for highly bound transporter substrates.

PURPOSE: To derive the theoretical basis for the extended clearance model of organ elimination following both oral and IV dosing, and critically analyze the approaches previously taken. METHODS: We derived from first principles the theoretical basis for the extended clearance concept of organ elimination following both oral and IV dosing and critically analyzed previous approaches. RESULTS: We point out a number of critical characteristics that have either been misinterpreted or not clearly presented in previously published treatments. First, the extended clearance concept is derived based on the well-stirred model. It is not appropriate to use alternative models of hepatic clearance. In analyzing equations, clearance terms are all intrinsic clearances, not total drug clearances. Flow and protein binding parameters should reflect blood measurements, not plasma values. In calculating the AUC(R)-factor following oral dosing, the AUC terms do not include flow parameters. We propose that calculations of AUC(R) may be a more useful approach to evaluate drug-drug and pharmacogenomic interactions than evaluating rate-determining steps. Through analyses of cerivastatin and fluvastatin interactions with cyclosporine we emphasize the need to characterize volume of distribution changes resulting from transporter inhibition/induction that can affect rate constants in PBPK models. Finally, we note that for oral doses, prediction of systemic and intrahepatic drug-drug interactions do not require knowledge of f(u,H) or K(p,uu) for substrates/victims. CONCLUSIONS: The extended clearance concept is a powerful tool to evaluate drug-drug interactions, pharmacogenomic and disease state variance but evaluating the AUC(R)-factor may provide a more valuable approach than characterizing rate-determining steps.

As explained by the free drug theory, the unbound fraction of drug has long been thought to drive the efficacy of a molecule. Thus, the fraction unbound term, or f(u), appears in equations for fundamental pharmacokinetic parameters such as clearance, and is used when attempting in vitro to in vivo extrapolation (IVIVE). In recent years though, it has been noted that IVIVE does not always yield accurate predictions, and that some highly protein bound ligands have more efficient uptake than can be explained by their unbound fractions. This review explores the evolution of f(u) terms included when implementing IVIVE, the concept of protein-facilitated uptake, and the mechanisms that have been proposed to account for facilitated uptake.

Cornelia de Lange Syndrome (CdLS) is a multiple congenital anomaly disorder resulting from mutations in genes that encode the core components of the cohesin complex, SMC1A, SMC3, and RAD21, or two of its regulatory proteins, NIPBL and HDAC8. HDAC8 is the human SMC3 lysine deacetylase required for cohesin recycling in the cell cycle. To date, 16 different missense mutations in HDAC8 have recently been identified in children diagnosed with CdLS. To understand the molecular effects of these mutations in causing CdLS and overlapping phenotypes, we have fully characterized the structure and function of five HDAC8 mutants: C153F, A188T, I243N, T311M, and H334R. X-ray crystal structures reveal that each mutation causes local structural changes that compromise catalysis and/or thermostability. For example, the C153F mutation triggers conformational changes that block acetate product release channels, resulting in only 2% residual catalytic activity. In contrast, the H334R mutation causes structural changes in a polypeptide loop distant from the active site and results in 91% residual activity, but the thermostability of this mutant is significantly compromised. Strikingly, the catalytic activity of these mutants can be partially or fully rescued in vitro by the HDAC8 activator N-(phenylcarbamothioyl)benzamide. These results suggest that HDAC8 activators might be useful leads in the search for new therapeutic strategies in managing CdLS.

Cornelia de Lange syndrome (CdLS) is amultisystemgenetic disorder with distinct facies, growth failure, intellectual disability, distal limb anomalies, gastrointestinal and neurological disease. Mutations in NIPBL, encoding a cohesin regulatory protein, account for >80% of cases with typical facies. Mutations in the core cohesin complex proteins, encoded by the SMC1A, SMC3 and RAD21 genes, together account for ̃5% of subjects, often with atypical CdLS features. Recently, we identified mutations in the X-linked gene HDAC8 as the cause of a small number of CdLS cases. Here, we report a cohort of 38 individuals with an emerging spectrum of features caused by HDAC8 mutations. For several individuals, the diagnosis of CdLS was not considered prior to genomic testing. Most mutations identified are missense and de novo. Many cases are heterozygous females, each with marked skewing of X-inactivation in peripheral blood DNA.Wealso identified eight hemizygous males who are more severely affected. The craniofacial appearance caused by HDAC8 mutations overlaps that of typical CdLS but often displays delayed anterior fontanelle closure, ocular hypertelorism, hooding of the eyelids, a broader nose and dental anomalies, which may be useful discriminating features. HDAC8 encodes the lysine deacetylase for the cohesin subunit SMC3 and analysis of the functional consequences of the missense mutations indicates that all cause a loss of enzymatic function. These data demonstrate that loss-of-function mutations in HDAC8 cause a range of overlapping human developmental phenotypes, including a phenotypically distinct subgroup of CdLS. © The Author 2014. Published by Oxford University Press. All rights reserved.Published by Oxford University Press. All rights reserved.

Predicting in vivo pharmacokinetic parameters such as clearance from in vitro data is a crucial part of the drug-development process. There is a commonly cited trend that drugs that are highly protein-bound and are substrates for hepatic uptake transporters often yield the worst predictions. Given this information, 11 different data sets using human microsomes and hepatocytes were evaluated to search for trends in accuracy, extent of protein binding, and drug classification based on the Biopharmaceutics Drug Disposition Classification System (BDDCS), which makes predictions about transporter effects. As previously reported, both in vitro systems (microsomes and hepatocytes) gave a large number of inaccurate results, defined as predictions falling more than 2-fold outside of in vivo values. The weighted average of the percentage of inaccuracy was 66.5%. BDDCS class 2 drugs, which are subject to transporter effects in vivo unlike class 1 compounds, had a higher percentage of inaccurate predictions and often had slightly larger bias. However, since the weighted average of the percentage of inaccuracy was still high in both classes (81.9% for class 2 and 62.3% for class 1), it may be currently hard to use BDDCS class to predict potential accuracy. The results of this study emphasize the need for improved in vitro to in vivo extrapolation experimental methods, as using physiologically based scaling is still not accurate, and BDDCS cannot currently help predict accurate results.

Histone deacetylases (HDACs) regulate cellular processes such as differentiation and apoptosis and are targeted by anticancer therapeutics in development and in the clinic. HDAC8 is a metal-dependent class I HDAC and is proposed to use a general acid-base catalytic pair in the mechanism of amide bond hydrolysis. Here, we report site-directed mutagenesis and enzymological measurements to elucidate the catalytic mechanism of HDAC8. Specifically, we focus on the catalytic function of Y306 and the histidine-aspartate dyads H142-D176 and H143-D183. Additionally, we report X-ray crystal structures of four representative HDAC8 mutants: D176N, D176N/Y306F, D176A/Y306F, and H142A/Y306F. These structures provide a useful framework for understanding enzymological measurements. The pH dependence of kcat/KM for wild-type Co(II)-HDAC8 is bell-shaped with two pKa values of 7.4 and 10.0. The upper pKa reflects the ionization of the metal-bound water molecule and shifts to 9.1 in Zn(II)-HDAC8. The H142A mutant has activity 230-fold lower than that of wild-type HDAC8, but the pKa1 value is not altered. Y306F HDAC8 is 150-fold less active than the wild-type enzyme; crystal structures show that Y306 hydrogen bonds with the zinc-bound substrate carbonyl, poised for transition state stabilization. The H143A and H142A/H143A mutants exhibit activity that is80000-fold lower than that of wild-type HDAC8; the buried D176N and D176A mutants have significant catalytic effects, with more subtle effects caused by D183N and D183A. These enzymological and structural studies strongly suggest that H143 functions as a single general base-general acid catalyst, while H142 remains positively charged and serves as an electrostatic catalyst for transition state stabilization.

The reversible acetylation of lysine to form N6-acetyllysine in the regulation of protein function is a hallmark of epigenetics. Acetylation of the positively charged amino group of the lysine side chain generates a neutral N-alkylacetamide moiety that serves as a molecular “switch” for the modulation of protein function and protein–protein interactions. We now report the analysis of 381 N6-acetyllysine side chain amide conformations as found in 79 protein crystal structures and 11 protein NMR structures deposited in the Protein Data Bank (PDB) of the Research Collaboratory for Structural Bioinformatics. We find that only 74.3% of N6-acetyllysine residues in protein crystal structures and 46.5% in protein NMR structures contain amide groups with energetically preferred trans or generously trans conformations. Surprisingly, 17.6% of N6-acetyllysine residues in protein crystal structures and 5.3% in protein NMR structures contain amide groups with energetically unfavorable cis or generously cis conformations. Even more surprisingly, 8.1% of N6-acetyllysine residues in protein crystal structures and 48.2% in NMR structures contain amide groups with energetically prohibitive twisted conformations that approach the transition state structure for cis-trans isomerization. In contrast, 109 unique N-alkylacetamide groups contained in 84 highly accurate small molecule crystal structures retrieved from the Cambridge Structural Database exclusively adopt energetically preferred trans conformations. Therefore, we conclude that cis and twisted N6-acetyllysine amides in protein structures deposited in the PDB are erroneously modeled due to their energetically unfavorable or prohibitive conformations. Proteins 2013; © 2012 Wiley Periodicals, Inc.

Summary 3-ω-Cyclohexyloctyl-2-hydroxy-1,4-naphthoquinone and 7-ω-cyclohexyloctyl-6-hydroxy-5,8-quinolinequinone inhibit the succinate-cytochrome c reductase and the succinate-coenzyme Q reductase in beef heart submitochondrial systems. These inhibitions could be reversed by coenzyme Q 6 . These analogs of coenzyme Q also inhibit the succinate-coenzyme Q reductase of intact mitochondria from the human heart.