To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi‐site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site‐related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi‐center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group. Multi‐site neuroimaging studies are hindered by differences in images acquired across multiple sites, often referred to as site effects. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for machine learning. After applying our proposed harmonization method CovBat, we find that machine learning methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group. This trend is especially prevalent in neuroimaging research where the reliability and generalizabilty of findings from the conventional single‐site studies are often limited by the ability to recruit and study sufficiently large and representative samples from the population. Many consortia have been formed to address such issues  methods. This approach has become a powerful tool for leveraging both functional and structural neuroimaging for research into pain  or distance‐based methods to unify distributions of measurements across sites. However, the GAN‐based harmonization methods have only been tested for harmonization of images and lack options to retain clinical associations of interest  and demonstrate the existence of site effects in covariance of structural imaging measures. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within‐site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to detect Siemens scanners after our proposed harmonization is applied, and that the CovBat‐harmonized data retain accurate prediction of disease group. We also assess the performance of the proposed method in simulated data, and again find that the method mitigates site effects and maintains detection of meaningful associations. Our results demonstrate the need to consider covariance in harmonization methods, and suggest a novel procedure that can be applied to better harmonize data from multi‐site imaging studies.22.1http://adni.loni.usc.edu/ which are processed using the ANTs longitudinal single‐subject template pipeline . Informed consent was obtained by all participants in the ADNI study. Institutional review boards approved the study at all of the contributing institutions.The data for this article consist of baseline scans from ADNI  headers for each scan. Specifically, subjects are considered to be acquired at the same site if they share the scanner location, scanner manufacturer, scanner model, head coil, and magnetic field strength. In total, this definition yields 142 distinct sites of which 78 had less than three subjects and were removed from analyses. The final sample consists of 505 subjects across 64 sites, with 213 subjects imaged on scanners manufactured by Siemens, 70 by Philips, and 222 by GE. The sample has a mean age of 75.3 (SD 6.70) and is comprised of 278 (55%) males, 115 (22.8%) AD patients, 239 (47.3%) late mild cognitive impairment (LMCI), and 151 (29.9%) cognitively normal (CN) individuals.The ADNI sample demographics are considerably different across sites, which precludes application of certain harmonization methods. For example,  scores to shift each within‐site covariance to the pooled covariance structure. We achieve this by approximating within‐site covariance structures using the PCs and PC scores obtained across all observations. We propose the CovBat algorithm, which accounts for the joint distribution of ComBat‐adjusted observations as follows:However, the error vectors Step 1. We first perform ComBat to remove the mean and variance shifts in the marginal distributions of the cortical thickness measures. Then, we additionally residualize with respect to the intercept and covariates to obtain ComBat‐adjusted residuals denoted eijComBat=eij1ComBateij2ComBat…eijpComBatT where p is the number of features. We then define these residuals using notation from Section i=1,2,…,M, j=1,2,…,ni, M is the number of sites, and ni is the number of subjects acquired at site i. α^v, xijT, β^v, γiv*, and δiv* are defined in Section Step 2. The eijComBat are assumed to have mean 0; their covariance matrices which we denote by ∑i, however, may differ across sites. We first perform principal components analysis (PCA) on the full data residuals and represent the full data covariance matrix as ∑=∑k=1qλkϕkϕkT where the rank q=min∑i=1Mnip, λk are the eigenvalues of ∑, and ϕk are the PCs obtained as the eigenvectors of ∑. In practice, PCA is performed on the sample covariance matrix ∑^ and we obtain estimated eigenvalues λ^k and eigenvectors ϕ^k. The ComBat‐adjusted residuals can then be expressed as eijComBat=∑k=1qξijkϕ^k where ξijk are the principal component scores.∑i to the pooled covariance across sites. Since our goal is to recover covariance structures resembling ∑, we approximate the within‐site covariance matrices as ∑^i=∑k=1qλ^ikϕ^kϕ^kT where λ^ik are within‐site eigenvalues estimated as the sample variance of the principal component scores λ^ik=∑j=1niξijk−∑j=1niξijk/ni2/ni−1 and ϕ^k are estimated from the full data covariance. This model assumes that the covariance site effect is contained within the variances of the PC scores with the principal components estimated from the full data. This assumption may not hold in some cases, but harmonization of these PC score variances will bring the within‐site covariance matrices closer to the pooled covariance. This is analogous to how ComBat brings site‐specific variance closer to the variance estimated using observations across all sites.We then aim to bring each within‐site covariance matrix Step 3. Thus, we posit:ϵijk∼N0τk2, τk is the error standard deviation, and μik, ρik are the center and scale parameters corresponding to principal components k=1,2,…K where K≤q is a tuning parameter chosen to capture the desired proportion of the variation in the observations. If K is chosen such that K=q, all principal components are harmonized. Note that this is similar to the ComBat model, applied to each of the k principal component scores instead of the original measures. We can then estimate each of the K pairs of center and scale parameters by finding the values that bring each site's mean and variance in scores to the pooled mean and variance, which we denote μ^ik) and ρ^ik. We then remove the site effect in the scores via ξijkCovBat=ξijk−μ^ik/ρ^ik.Step 4. We obtain CovBat‐adjusted residuals eijCovBat=eij1CovBateij2CovBat…eijpCovBatT by projecting the adjusted scores back into the residual space via,We then add the intercepts and covariates effects estimated in Step 1 to obtain CovBat‐adjusted observations2.42.4.1yij, i=1,2,3, j=1,2,…,ni be vectors of length p representing simulated cortical thickness values for three sites, each with ni observations. The yij are generated using the following model:xij is a simulated diagnosis variable drawn from a Bernoulli distribution with probability 0.25, α is the first p/2 elements in each hemisphere from the sample mean vector of Scanner B observations in the ADNI data, β is the vector of simulated diagnosis effects on the mean, and eij is the vector of error terms. We simulate mean and variance site effects based on the assumptions of ComBat and CovBat. The mean site effects γi=γi1γi2…γipT are vectors drawn from independent and identically distributed (i.i.d.) normal distributions with mean zero and standard deviation 0.1. The variance site effects δi=δi1δi2…δipT are vectors drawn from site‐specific inverse gamma distributions with chosen parameters. For our simulations, we chose to distinguish the site‐specific scaling factors by assuming δ1v∼i.i.dInverse Gamma46,50, δ2v∼i.i.dInverse Gamma51,50, and δ3v∼i.i.dInverse Gamma56,50 for v=1,2,…,p.Let 2.5p=62, we set the underlying covariance S as the sample correlation matrix of cortical thickness observations in the ADNI data, with eigendecomposition S=∑k=162λ^kϕ^kϕ^kT. We then generate error terms eij that contain site‐specific shifts in the first eigenvalue viaci controls the severity of the covariance shift. For our simulations, we set c1=−1/2, c2=0, and c3=1/2 so that the pooled covariance structure is equivalent to S. We choose βi=−0.5 for p/4 regions of interest in both the left and right hemispheres to associate the simulated diagnosis with decreases in mean simulated cortical thicknesses.We first assess whether CovBat can recover the underlying covariance structure when the covariance site effects are captured by its PC directions. We refer to this simulation setting as the Simple Covariance Effects simulation. For K in the generation of error termsci takes the same values as previously, c1=−1/2, c2=0, and c3=1/2. We simulate datasets while choosing K as 2, 6, and 12 PCs and evaluate how harmonization influences detection of site via ML.We also investigate how the rank of the covariance effect influences CovBat harmonization results. We modify the rank of the simple covariance effect by varying 2.6Ωi and due to simulated diagnosis as Ψ. For p=62, the error terms eij are now generated via∑ij=S+xijΨ+Ωi, S is the sample correlation matrix of cortical thickness observations in the ADNI data, Ψ is a chosen diagnosis‐driven covariance shift matrix, and Ωi are site‐specific covariance shift matrices. The matrices Dij=diagdij where dijk=1/∑ijkk for k=1,2,…,62 ensure that these covariance effects do not modify the marginal variances of eij. To constrain the covariance matrices to be positive definite, we set the negative eigenvalues of ∑ij equal to a small constant, 10−12. For p<62, we instead generate eij from the p×p submatrices of Dij∑ijDij constructed from the rows and columns corresponding to the first p/2 features in each hemisphere. In this simulation scenario, S is no longer the pooled covariance structure since the covariance site effects Ωi can take any form and these site‐specific covariance structures do not necessarily combine across sites to resemble S. Instead of focusing on recovery of an underlying structure, we evaluate site effects throughout these simulations via ML.To evaluate how CovBat performs when the covariance site effects are not easily captured by the principal components and when the simulated diagnosis may affect covariance, we modify the simulation to incorporate high‐rank covariance shifts due to site via We design four simulation experiments to test how CovBat performs in multiple settings with varied covariance effects. In our ComBat simulation, we generate data without any covariance site effect from a model that resembles the ComBat model. In the Diagnosis Affects Mean simulation, we then introduce a covariance site effect to assess if CovBat can outperform ComBat in harmonization of covariance. We next introduce a simulated diagnosis effect on covariance in the Diagnosis Affects Covariance simulation, which better illustrates how the detection of the simulated diagnosis is affected by related covariance effects. Finally, we design a Covariance Only simulation, which has no site or diagnosis effects in mean or variance, but still contains site and diagnosis effects in covariance. This final simulation illustrates how effects on covariance can influence ML results and be addressed through our proposed harmonization method.βi=−0.5 for p/4 regions of interest in both the left and right hemispheres to impose that about half of the ROIs are negatively associated with the simulated diagnosis. We also choose the Ωi and Ψ to be 62×62 zero matrices to ensure that the covariance does not depend on site or the simulated diagnosis.In the ComBat simulation, we impose site effects in mean and variance while having the simulated diagnosis affect only the mean of the observations. We choose β and Ψ as in the ComBat simulation. However, we choose Ωi to be distinct 62×62 high‐rank matrices to distinguish covariance structures across sites. These Ωi are constructed by downsampling three distinct images to 62 × 62 pixels and then scaling the values so that the diagonal is a vector of ones. These matrices thus have different eigenvectors from each other and also from the sample covariance matrix S, which simulates complex site covariance structures which do not have the same eigenvectors.In the Diagnosis Affects Mean simulation, we again impose that the simulated diagnosis only affects the mean of the measurements but also introduce a site effect in covariance. We keep the same β value as in the ComBat and diagnosis affects mean simulations but choose Ψ to be related to Ω3 to force confounding of Site 3 and diagnosis effects on covariance. To achieve this, we set Ψ=−3/4Ω3.In the Diagnosis Affects Covariance simulation, we assume that the simulated diagnosis affects not only mean, but also covariance. We choose the diagnosis effect on covariance to be proportional to a site's covariance shift. This scenario represents a situation where detection of the diagnosis using ML could be highly influenced by the presence of site effects. We use the same γ=0 and δ=1 to remove site effects in mean and variance while also using the same Ωi as in the Diagnosis Affects Mean and Diagnosis Affects Covariance simulations. Furthermore, we modify the diagnosis effect by setting β=0 while keeping Ψ=−3/4Ω3 for the diagnosis effect in covariance.In the Covariance Only simulation, we assume that both site and the diagnosis influence the covariance, not the mean or variance. We fix 2.7S. We then report the average across sites before and after harmonization, where CovBat harmonization is performed on PCs that explain 95% of the variation.In our simple covariance effects simulation with rank one covariance effects, we generate 1,000 datasets across varying sample sizes and number of features to evaluate recovery of the underlying covariance structure. For each site, we calculate the average Frobenius distance across datasets between each sample within‐site covariance matrix and the true covariance randomForest package on CRAN with default parameters using R version 3.6.1. Lower AUC for detection of Site 1 and higher AUC for detection of the simulated diagnosis indicate improved harmonization. CovBat harmonization is performed on PCs that explain 95% of the variation. For prediction of Site 1, we avoid the possibility that site could be detected through the simulated diagnosis by using linear models to residualize out disease from each simulated cortical thickness value. For simulated datasets where the training set does not contain observations with the simulated diagnosis, the random forest algorithm cannot be trained so we generate another dataset to replace it. We repeat these ML experiments while varying the number of PCs included in the CovBat harmonization. For CovBat including PCs that explain 95% of the variation, we also perform MANOVA for testing associations with site and simulated diagnosis and report the rejection rate at a type I error rate of 0.05 across the 1,000 datasets.For the other simulation settings, we generate 1,000 datasets for several choices of within‐site sample size and number of features and perform experiments to evaluate detection of site and disease using ML. For each dataset, we (a) randomly split the sample into 50% training data and 50% validation data, (b) train a random forests algorithm to recognize if the observations either are from Site 1 or have the simulated diagnosis, and (c) assess predictive performance on the testing data. Our random forests algorithm is implemented via the 2.8S in Section We first perform simulations to assess whether either harmonization method can recover the underlying covariance structure in our simple simulation setting and harmonize covariance matrices generally. For ComBat and CovBat, we include our simulated diagnosis status as a covariate. We apply our CovBat method using the number of PCs that explain 95% of the variation. In the Simple Covariance Effects simulation, Figure 2.9We then evaluate CovBat through two ML experiments across all simulation settings considered. For our main analyses, we simulate 62 cortical thicknesses for 250 subjects per site. We begin by examining a ComBat simulation, where we generate data from the original ComBat model. That is, we impose mean and variance site effects while also simulating a diagnosis that has an effect on the mean. Figure We then incorporate a diagnosis effect on covariance in our Diagnosis Affects Covariance simulation and show that CovBat reduces detection of site Figure  and main2.10ni=25) and larger number of features (p≥48) we observe that CovBat can inflate detection of site. We then conduct additional analyses to assess the robustness of CovBat to reductions in sample size per site and number of features. In Table 2.11To better inform the choice of PCs in CovBat, we evaluate the simulation results obtained across varying number of PCs. For simple covariance effects with varying low rank structures, 33.1We evaluate the performance of harmonization in the ADNI data by comparing three different approaches. First, we test the original unharmonized data in our subsequent experiments. Second, we produce ComBat‐harmonized data by performing ComBat while including age, sex, and diagnosis status as covariates. Third, we obtain CovBat‐harmonized data by running CovBat with varying number of PCs, while also including age, sex, and diagnosis status. For our primary analyses, we run CovBat while including 37 PCs, which cumulatively explain 95% of variation. We include additional analyses evaluating how our results vary with the choice of PCs.3.2randomForest package on CRAN with default parameters using R version 3.6.1. We train separate models for unharmonized, ComBat‐harmonized, and CovBat‐harmonized data where both harmonization methods are performed including age, sex, and diagnosis status as covariates. We perform steps (i)–(iii) 100 times for each dataset and report the mean AUC along with standard deviation. For these experiments, lower AUC for detection of scanner manufacturer and higher AUC for detection of clinical covariates would indicate improved harmonization. For prediction of scanner manufacturer, we avoid the possibility that scanner could be detected through the covariates age, sex, and disease status by residualizing out these variables from each cortical thickness value via linear models. To assess performance across choices of PCs in CovBat, we repeat our ML experiments across varying numbers of PCs explaining between 44 and 100% of the variation.Using the full ADNI sample, we evaluate whether the harmonization procedures affect the results of ML using the neuroimaging measures as patterns for a prediction algorithm. We achieve this through a Monte Carlo split‐sample experiment where we (i) randomly split the subjects into 50% training set and 50% validation set, (ii) train a random forests algorithm to detect either scanner manufacturer or a binary clinical covariate, and (iii) assess predictive performance on the validation set via AUC. Our random forests algorithm is implemented via the 3.3p‐values for these associations before and after harmonization.We also evaluate the harmonization methods using multivariate analysis of variance (MANOVA). MANOVA tests for differences in mean across groups in multivariate data, but is known to be sensitive to differences in covariance across groups. We perform MANOVA across scanner manufacturer, sex, and diagnosis status using Pillai's trace, which is known to be more robust to inhomogeneity in covariance than other alternative test statistics Olson, . We repo3.4We first examine the empirical covariance of the ADNI data before and after harmonization. To evaluate site differences, we investigate the three largest ADNI sites. Site A consists of 23 subjects acquired on a Siemens Symphony 1.5T scanner while Sites B and C each consist of 20 subjects acquired on GE Signa Excite 1.5T scanners. See Table We also provide quantitative comparisons for pairwise distances across sites before and after harmonization in Table 3.5To evaluate the potential impact of site effects in covariance using ML, we conduct a Monte‐Carlo split‐sample experiment for prediction of scanner manufacturer labels using all 213 ADNI subjects before and after harmonization with existing methods. We train using data harmonized with the state‐of‐the‐art ComBat method and our proposed method, CovBat. Figure 3.6It is well‐known that cortical thickness differs substantially by sex and AD status . Reference implementations for ComBat are available in R and Matlab (https://github.com/Jfortin1/ComBatHarmonization) and in Python (https://github.com/ncullen93/neuroCombat). Our R implementation of CovBat runs in reasonable time, even in large samples. We generate observations with 62 features with varying sample sizes. On a MacBook Pro  with a 2.3 GHz 8‐Core Intel Core i9 and 32 GB 2667 MHz DDR4 memory, CovBat runs in 0.014 s for 100 samples, 0.090 s for 1,000 samples, 1.088 s for 10,000 samples, and 12.235 seconds for 100,000 samples.All of the postprocessing analysis was performed in the R statistical software (V3.6.1). CovBat is available for both R and Python  and number of features (p) in the absence of predictor effects on covariance. For each of 1,000 simulations, the data is randomly split into 50% training and 50% validation then a random forests algorithm is trained using the training set to predict either Site 1 or the presence of the binary predictor. The median AUC across these simulations are reported with lower and upper quartiles displayed in parentheses. Scenarios where CovBat outperforms ComBat are colored in blue.Table S2: AUC results from MVPA simulations for detection of site across multiple sample sizes (n) and number of features (p) where the predictor has effects on covariance. For each of 1,000 simulations, the data is randomly split into 50% training and 50% validation then a random forests algorithm is trained using the training set to predict either Site 1 or the presence of the binary predictor. The median AUC across these simulations are reported with lower and upper quartiles displayed in parentheses. Scenarios where CovBat outperforms ComBat are colored in blue.Table S3: AUC results from MVPA simulations for detection of simulated diagnosis across multiple sample sizes (n) and number of features (p) where the predictor has effects on covariance. For each of 1,000 simulations, the data is randomly split into 50% training and 50% validation then a random forests algorithm is trained using the training set to predict either Site 1 or the presence of the binary predictor. The median AUC across these simulations are reported with lower and upper quartiles displayed in parentheses.Table S4: MANOVA rejection rates for associations with site and diagnosis status across multiple sample sizes (n) and number of features (p) in the Diagnosis affects Mean simulations. For each of 1,000 simulations, MANOVA is performed separately for site and diagnosis using Pillai's trace. The rejection rate across these simulations is reported as the percentage of p‐values less than .05.Table S5: MANOVA rejection rates for associations with site and diagnosis status across multiple sample sizes (n) and number of features (p) in the Diagnosis affects Covariance simulations. For each of 1,000 simulations, MANOVA is performed separately for site and diagnosis using Pillai's trace. The rejection rate across these simulations is reported as the percentage of p‐values less than .05. Scenarios where the rejection rate is less than 0.05 are colored in blue.Table S6: MANOVA rejection rates for associations with site and diagnosis status across multiple sample sizes (n) and number of features (p) in the Simple Covariance Effect simulations. For each of 1,000 simulations, MANOVA is performed separately for site and diagnosis using Pillai's trace. The rejection rate across these simulations is reported as the percentage of p‐values less than .05. Scenarios where the rejection rate is less than 0.05 are colored in blue.Click here for additional data file.


Obtusofolin is the major active ingredient of Catsia tora L., which possesses the activity of improving eyesight and protecting the optic nerve. Investigation on the interaction of obtusofolin with cytochrome P450 enzymes (CYP450s) could provide a reference for the clinical application of obtusofolin.The effect of obtusofolin on the activity of CYP450s was investigated in the presence of 100 μM obtusofolin in pooled human liver microsomes (HLMs) and fitted with the Lineweaver–Burk plots to characterize the specific inhibition model and kinetic parameters.50 values of 17.1 ± 0.25, 10.8 ± 0.13, and 15.5 ± 0.16 μM, respectively. The inhibition of CYP3A4 was best fitted with the non-competitive inhibition model with the Ki value of 8.82 μM. While the inhibition of CYP2C9 and 2E1 was competitive with the Ki values of 5.54 and 7.79 μM, respectively. After incubating for 0, 5, 10, 15, and 30 min, the inhibition of CYP3A4 was revealed to be time-dependent with the KI value of 4.87 μM− 1 and the Kinact value of 0.0515 min− 1.Obtusofolin was found to significantly inhibited the activity of CYP3A4, 2C9, and 2E1. In the presence of 0, 2.5, 5, 10, 25, 50, and 100 μM obtusofolin, the inhibition of these CYP450s showed a dose-dependent manner with the ICThe in vitro inhibitory effect of obtusofolin implying the potential drug-drug interaction between obtusofolin and corresponding substrates, which needs further in vivo validations. Catsia tora Linn can be used as a food or medicine in traditional Chinese medicine, which possesses the activity of improving eyesight and protecting the optic nerve , The enzyme kinetic parameters were obtained by the least-squares linear regression. The inhibition data were fitted with non-linear regression according to the following equation:I is the concentration of the compound, Ki is the inhibition constant, S is the concentration of the substrate and Km is the substrate concentration at half the maximum velocity (Vmax) of the reaction. The mechanism of the inhibition was inspected using the Lineweaver–Burk plots and the enzyme inhibition models. The data comparison was performed using the Student’s t-test and performed using IBM SPSS statistics 20 .where P < 0.05, Fig. P < 0.05, Fig. 50 values of CYP3A4, 2C9, and 2E1 were obtained as 17.1 ± 0.25, 10.8 ± 0.13, and 15.5 ± 0.16 μM, respectively.Corresponding inhibitors dramatically reduced the activity of all CYP isoforms , whereas the inhibitory effect on CYP2C9 and 2E1 was not affected. Furthermore, the time-dependent manner was characterized in the presence of various obtusofolin concentrations. During the time-dependent inhibition of CYP3A4 by obtusofolin, the Catsia tora L., which has been widely applied in the ophthalmology prescription in the clinic [The interaction between CYP450s and various drugs has drawn special attention in the past decades. Numerous evidence has revealed a number of drugs that affected the activity of CYP450s and induced adverse interactions –16. Obtue clinic . ObtusofHere, obtusofolin was found to inhibit the activity of CYP3A4, 2C9, and 2E1 in the present study. These CYPs were involved in the metabolism of a large number of drugs, the inhibitory effect of obtusofolin, therefore, implied the potential drug-drug interaction . Previou50 values of obtusofolin in the inhibition of CYP3A4, 2C9, and 2E1 [In previous studies focused on the pharmacokinetic profile of obtusofolin, the maximum of 1.3 mg/kg obtusofolin in rats was 152.5 ± 62.3 ng/mL, which is much less than the IC and 2E1 , indicat50 values, and the incubation time is an important impactor during the inhibition of CYP3A4. The inhibitory effect of obtusofolin implying the potential drug-drug interaction between obtusofolin and drugs metabolized by these CYPs, which needs further in vivo validations.Taken together, obtusofolin was identified as a competitive inhibitor of CYP2C9 and 2E1, and a non-competitive inhibitor of CYP3A4. The inhibition of these CYPs was conducted in a dose-dependent manner with various IC