Elucidating nonlinear pharmacokinetics of telmisartan: Integration of target-mediated drug disposition and OATP1B3-mediated hepatic uptake in a physiologically based model.

Telmisartan, a selective inhibitor of angiotensin II receptor type 1 (AT1), demonstrates nonlinear pharmacokinetics (PK) when orally administered in ascending doses to healthy volunteers, but the underlying mechanisms remain unclear. This study presents a physiologically based pharmacokinetic model integrated with target-mediated drug disposition (TMDD-PBPK model) to explore the mechanism of its nonlinear PK. We employed the Cluster-Gauss Newton method for top-down analysis, estimating the in vivo Km,OATP1B3 (Michaelis-Menten constant for telmisartan hepatic uptake via Organic Anion Transporting Polypeptide 1B3) to be 2.0-5.7 nM. This range is significantly lower than the reported in vitro value of 810 nM, obtained in 0.3% human serum albumin (HSA) conditions. Further validation was achieved through in vitro assessment in plated human hepatocytes with 4.5% HSA, showing a Km of 4.5 nM. These results underscore the importance of albumin-mediated uptake effect for the hepatic uptake of telmisartan. Our TMDD-PBPK model, developed through a "middle-out" approach, underwent sensitivity analysis to identify key factors in the nonlinear PK of telmisartan. We found that the nonlinearity in the area under the concentration-time curve (AUC) and/or maximum concentration (Cmax) of telmisartan is sensitive to Km,OATP1B3 across all dosages. Additionally, the dissociation constant (Kd) for telmisartan binding to the AT1 receptor, along with its receptor abundance, notably influences PK at lower doses (below 20 mg). In conclusion, the nonlinear PK of telmisartan appears primarily driven by hepatic uptake saturation across all dose ranges and by AT1-receptor binding saturation, notably at lower doses.

Kinetic modeling of the plasma pharmacokinetic profiles of ADAMTS13 fragment and its Fc-fusion counterpart in mice.

Introduction: Fusion of the fragment crystallizable (Fc) to protein therapeutics is commonly used to extend the circulation time by enhancing neonatal Fc-receptor (FcRn)-mediated endosomal recycling and slowing renal clearance. This study applied kinetic modeling to gain insights into the cellular processing contributing to the observed pharmacokinetic (PK) differences between the novel recombinant ADAMTS13 fragment (MDTCS) and its Fc-fusion protein (MDTCS-Fc). Methods: For MDTCS and MDTCS-Fc, their plasma PK profiles were obtained at two dose levels following intravenous administration of the respective proteins to mice. The plasma PK profiles of MDTCS were fitted to a kinetic model with three unknown protein-dependent parameters representing the fraction recycled (FR) and the rate constants for endocytosis (kup, for the uptake into the endosomes) and for the transfer from the plasma to the interstitial fluid (kpi). For MDTCS-Fc, the model was modified to include an additional parameter for binding to FcRn. Parameter optimization was done using the Cluster Gauss-Newton Method (CGNM), an algorithm that identifies multiple sets of approximate solutions ("accepted" parameter sets) to nonlinear least-squares problems. Results: As expected, the kinetic modeling results yielded the FR of MDTCS-Fc to be 2.8-fold greater than that of MDTCS (0.8497 and 0.3061, respectively). In addition, MDTCS-Fc was predicted to undergo endocytosis (the uptake into the endosomes) at a slower rate than MDTCS. Sensitivity analyses identified the association rate constant (kon) between MDTCS-Fc and FcRn as a potentially important factor influencing the plasma half-life in vivo. Discussion: Our analyses suggested that Fc fusion to MDTCS leads to changes in not only the FR but also the uptake into the endosomes, impacting the systemic plasma PK profiles. These findings may be used to develop recombinant protein therapeutics with extended circulation time.

Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models.

Physiologically-based pharmacokinetic (PBPK) models can be challenging to work with because they can have too many parameters to identify from observable data. The profile likelihood method can help solve this issue by determining parameter identifiability and confidence intervals, but it involves repetitive parameter optimizations that can be time-consuming. The Cluster Gauss-Newton method (CGNM) is a parameter estimation method that efficiently searches through a wide range of parameter space. In this study, we propose a method that approximates the profile likelihood by reusing intermediate computation results from CGNM, allowing us to obtain the upper bounds of the profile likelihood without conducting additional model evaluation. This method allows us to quickly draw approximate profile likelihoods for all unknown parameters. Additionally, the same approach can be used to draw two-dimensional profile likelihoods for all parameter combinations within seconds. We demonstrate the effectiveness of this method on three PBPK models.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

A 20-Year Research Overview: Quantitative Prediction of Hepatic Clearance Using the In Vitro-In Vivo Extrapolation Approach Based on Physiologically Based Pharmacokinetic Modeling and Extended Clearance Concept.

Understanding the extended clearance concept and establishing a physiologically based pharmacokinetic (PBPK) model are crucial for investigating the impact of changes in transporter and metabolizing enzyme abundance/functions on drug pharmacokinetics in blood and tissues. This mini-review provides an overview of the extended clearance concept and a PBPK model that includes transporter-mediated uptake processes in the liver. In general, complete in vitro and in vivo extrapolation (IVIVE) poses challenges due to missing factors that bridge the gap between in vitro and in vivo systems. By considering key in vitro parameters, we can capture in vivo pharmacokinetics, a strategy known as the top-down or middle-out approach. We present the latest progress, theory, and practice of the Cluster Gauss-Newton method, which is used for middle-out analyses. As examples of poor IVIVE, we discuss "albumin-mediated hepatic uptake" and "time-dependent inhibition" of OATP1Bs. The hepatic uptake of highly plasma-bound drugs is more efficient than what can be accounted for by their unbound concentration alone. This phenomenon is referred to as "albumin-mediated" hepatic uptake. IVIVE was improved by measuring hepatic uptake clearance in vitro in the presence of physiologic albumin concentrations. Lastly, we demonstrate the application of Cluster Gauss-Newton method-based analysis to the target-mediated drug disposition of bosentan. Incorporating saturable target binding and OATP1B-mediated hepatic uptake into the PBPK model enables the consideration of nonlinear kinetics across a wide dose range and the prediction of receptor occupancy over time. SIGNIFICANCE STATEMENT: There have been multiple instances where researchers' endeavors to unravel the underlying mechanism of poor in vitro-in vivo extrapolation have led to the discovery of previously undisclosed truths. These include 1) albumin-mediated hepatic uptake, 2) the target-mediated drug disposition in small molecules, and 3) the existence of a trans-inhibition mechanism by inhibitors for OATP1B-mediated hepatic uptake of drugs. Consequently, poor in vitro-in vivo extrapolation and the subsequent inquisitiveness of scientists may serve as a pivotal gateway to uncover hidden mechanisms.

EEG resting-state networks in Alzheimer's disease associated with clinical symptoms.

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Predicting In Vivo Target Occupancy (TO) Profiles via Physiologically Based Pharmacokinetic-TO Modeling of Warfarin Pharmacokinetics in Blood: Importance of Low Dose Data and Prediction of Stereoselective Target Interactions.

Warfarin is well recognized for its high-affinity and capacity-limited binding to the pharmacological target and undergoes target-mediated drug disposition. Here, we developed a physiologically based pharmacokinetic (PBPK) model that incorporated saturable target binding and other reported hepatic disposition components of warfarin. The PBPK model parameters were optimized by fitting to the reported blood pharmacokinetic (PK) profiles of warfarin with no stereoisomeric separation after oral dosing of racemic warfarin (0.1, 2, 5, or 10 mg) using the Cluster Gauss-Newton method (CGNM). The CGNM-based analysis yielded multiple "accepted" sets for six optimized parameters, which were then used to simulate the warfarin blood PK and in vivo target occupancy (TO) profiles. When further analyses examined the impact of dose selection on uncertainty in parameter estimation by the PBPK modeling, the PK data from 0.1 mg dose (well below target saturation) was important in practically identifying the target binding-related parameters in vivo. When stereoselective differences were incorporated for both hepatic disposition and target interactions, our PBPK modeling predicted that R-warfarin (of slower clearance and lower target affinity than S-warfarin) contributes to TO prolongation after oral dosing of racemic warfarin. Our results extend the validity of the approach by which the PBPK-TO modeling of blood PK profiles can yield TO prediction in vivo (applicable to the drugs with targets of high affinity and abundance and limited distribution volume via nontarget interactions). Our findings support that model-informed dose selection and PBPK-TO modeling may aid in TO and efficacy assessment in preclinical and clinical phase 1 studies. SIGNIFICANCE STATEMENT: The current physiologically based pharmacokinetic modeling incorporated the reported hepatic disposition components and target binding of warfarin and analyzed the blood pharmacokinetic (PK) profiles from varying warfarin doses, practically identifying target binding-related parameters in vivo. By implementing the stereoselective differences between R- and S-warfarin, our analysis predicted the role of R-warfarin in prolonging overall target occupancy. Our results extend the validity of analyzing blood PK profiles to predict target occupancy in vivo, which may guide efficacy assessment.

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies.

Coproporphyrin I (CP-I) is an endogenous biomarker supporting the prediction of drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically-based pharmacokinetic (PBPK) model for CP-I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP-I were estimated using the cluster Gauss-Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least-squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CLint,all ), the rate of biosynthesis (vsyn ), and the OATP1B inhibition constant of RIF(Ki,u,OATP ) were estimated by fitting to the observed CP-I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CLint,all , Ki,u,OATP , and vsyn were sensitive parameters. The obtained Ki,u,OATP for CP-I was 5.0- and 2.8-fold lower than that obtained for statins, confirming our previous findings describing substrate-dependent Ki,u,OATP values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP-I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP-I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP-I.

Cross-Species Translation of Biophase Half-Life and Potency of GalNAc-Conjugated siRNAs.

Small interfering RNAs (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs to treat liver diseases. Understanding how pharmacokinetics and pharmacodynamics translate is pivotal for in vivo study design and human dose prediction. However, the literature is sparse on translational data for this modality, and pharmacokinetics in the liver is seldom measured. To overcome these difficulties, we collected time-course biomarker data for 11 GalNAc-siRNAs in various species and applied the kinetic-pharmacodynamic modeling approach to estimate the biophase (liver) half-life and the potency. Our analysis indicates that the biophase half-life is 0.6-3 weeks in mouse, 1-8 weeks in monkey, and 1.5-14 weeks in human. For individual siRNAs, the biophase half-life is 1-8 times longer in human than in mouse, and generally 1-3 times longer in human than in monkey. The analysis indicates that the siRNAs are more potent in human than in mouse and monkey.

Bilirubin levels and kidney function decline: An analysis of clinical trial and real world data.

OBJECTIVE: To evaluate if previously found associations between low serum bilirubin concentration and kidney function decline is independent of hemoglobin and other key confounders. RESEARCH DESIGN AND METHODS: Clinical trial data from the SAVOR-TIMI 53 trial as well as the UK primary care electronic healthcare records, Clinical Practice Research Datalink (CPRD), were used to construct three cohorts of patients at risk of chronic kidney disease (CKD). The randomized clinical trial (RCT) cohort from the subset of SAVOR-TIMI 53 trial consisted of 10,555 type-2 diabetic patients with increased risk of cardiovascular disease. The two observational data cohorts from CPRD consisted of 71,104 newly diagnosed type-2 diabetes (CPRD-DM2) and 82,065 newly diagnosed hypertensive (CPRD-HT) patients without diabetes. Cohorts were stratified according to baseline circulating total bilirubin levels to determine association on the primary end point of a 30% reduction from baseline in estimated glomerular filtration rate (eGFR) and the secondary end point of albuminuria. RESULTS: The confounder adjusted hazard ratios of the subpopulation with lower than median bilirubin levels compared to above median bilirubin levels for the primary end point were 1.18 (1.02-1.37), 1.12 (1.05-1.19) and 1.09 (1.01-1.17), for the secondary end point were 1.26 (1.06-1.52), 1.11 (1.01-1.21) and 1.18 (1.01-1.39) for SAVOR-TIMI 53, CPRD-DM2, CPRD-HT, respectively. CONCLUSION: Our findings are consistent across all cohorts and endpoints: lower serum bilirubin levels are associated with a greater kidney function decline independent of hemoglobin and other key confounders. This suggests that increased monitoring of kidney health in patients with lower bilirubin levels may be considered, especially for diabetic patients.

Physiologically-based pharmacokinetic model-based translation of OATP1B-mediated drug-drug interactions from coproporphyrin I to probe drugs.

The accurate prediction of OATP1B-mediated drug-drug interactions (DDIs) is challenging for drug development. Here, we report a physiologically-based pharmacokinetic (PBPK) model analysis for clinical DDI data generated in heathy subjects who received oral doses of cyclosporin A (CysA; 20 and 75 mg) as an OATP1B inhibitor, and the probe drugs (pitavastatin, rosuvastatin, and valsartan). PBPK models of CysA and probe compounds were combined assuming inhibition of hepatic uptake of endogenous coproporphyrin I (CP-I) by CysA. In vivo Ki of unbound CysA for OATP1B (Ki,OATP1B ), and the overall intrinsic hepatic clearance per body weight of CP-I (CLint,all,unit ) were optimized to account for the CP-I data (Ki,OATP1B , 0.536 ± 0.041 nM; CLint,all,unit , 41.9 ± 4.3 L/h/kg). DDI simulation using Ki,OATP1B reproduced the dose-dependent effect of CysA (20 and 75 mg) and the dosing interval (1 and 3 h) on the time profiles of blood concentrations of pitavastatin and rosuvastatin, but DDI simulation using in vitro Ki,OATP1B failed. The Cluster Gauss-Newton method was used to conduct parameter optimization using 1000 initial parameter sets for the seven pharmacokinetic parameters of CP-I (ß, CLint, all , Fa Fg , Rdif , fbile , fsyn , and vsyn ), and Ki,OATP1B and Ki,MRP2 of CysA. Based on the accepted 546 parameter sets, the range of CLint, all and Ki,OATP1B was narrowed, with coefficients of variation of 12.4% and 11.5%, respectively, indicating that these parameters were practically identifiable. These results suggest that PBPK model analysis of CP-I is a promising translational approach to predict OATP1B-mediated DDIs in drug development.

A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer's dementia.

BACKGROUND AND PURPOSE: An early and accurate diagnosis of Dementia with Lewy bodies (DLB) is critical because treatments and prognosis of DLB are different from Alzheimer's disease (AD). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries. METHODS: In a prospective multicenter study, patients with probable DLB or with probable AD were enrolled in a 1:1 ratio. A continuous EEG segment of 150 seconds was recorded, and the EEG data was processed using MC-004, the EEG-based machine learning algorithm, with all clinical information blinded except for age and gender. RESULTS: Eighteen patients with probable DLB and 21 patients with probable AD were the included for the analysis. The performance of MC-004 differentiating probable DLB from probable AD was 72.2% (95% CI 46.5-90.3%) for sensitivity, 85.7% (63.7-97.0%) for specificity, and 79.5% (63.5-90.7%) for accuracy. When limiting to subjects taking ≤5 mg donepezil, the sensitivity was 83.3% (95% CI 51.6-97.9), the specificity 89.5% (66.9-98.7), and the accuracy 87.1% (70.2-96.4). CONCLUSIONS: MC-004, the EEG-based machine learning algorithm, was able to discriminate between DLB and AD with fairly high accuracy. MC-004 is a promising biomarker for DLB, and has the potential to improve the detection of DLB in a diagnostic process.

Assessing reproducibility and utility of clustering of patients with type 2 diabetes and established CV disease (SAVOR -TIMI 53 trial).

OBJECTIVE: To assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease. METHODS: The cardiovascular outcome trial SAVOR-TIMI 53 (n = 16,492) was used. Analyses focused on T2D patients with established CV disease. Unsupervised machine learning technique called "k-means clustering" was used to divide patients into subtypes. K-means clustering including HbA1c, age of diagnosis, BMI, HOMA2-IR and HOMA2-B was used to assign clusters to the following diabetes subtypes: severe insulin deficient diabetes (SIDD); severe insulin-resistant diabetes (SIRD); mild obesity-related diabetes (MOD); mild age-related diabetes (MARD). We refer these subtypes as "clustering-based diabetes subtypes". A simulation study using randomly generated data was conducted to understand how correlations between the above variables influence the formation of the cluster-based diabetes subtypes. The predictive utility of clustering-based diabetes subtypes for CV events (3-point MACE), renal function reduction (eGFR decrease >30%) and diabetic disease progression (introduction of additional anti-diabetic medication) were compared with conventional risk scores. Hazard ratios (HR) were estimated by Cox-proportional hazard models. RESULTS: In the SAVOR-TIMI 53 trial based dataset, the percentage of the clustering-based T2D subtypes were; SIDD (18%), SIRD (17%), MOD (29%), MARD (37%). Using the simulated dataset, the diabetes subtypes could be largely reproduced from a log-normal distribution when including known correlations between variables. The predictive utility of clustering-based diabetic subtypes on CV events, renal function reduction, and diabetic disease progression did not show an advantage compared to conventional risk scores. CONCLUSIONS: The consistent reproduction of four clustering-based T2D subtypes can be explained by the correlations between the variables used for clustering. Subtypes of T2D based on clustering had limited advantage compared to conventional risk scores to predict clinical outcome in patients with T2D and established CV disease.

The Use of Subgroup Disproportionality Analyses to Explore the Sensitivity of a Global Database of Individual Case Safety Reports to Known Pharmacogenomic Risk Variants Common in Japan.

INTRODUCTION: Genetic variations of enzymes that affect the pharmacokinetics and hence effects of medications differ between ethnicities, resulting in variation in the risk of adverse drug reactions (ADR) between different populations. Previous work has demonstrated that risk-group considerations can be incorporated into approaches of statistical signal detection. It is unknown whether databases of individual case safety reports (ICSRs) are sensitive to pharmacogenomic differences between populations. OBJECTIVE: The aim of this study was to explore the sensitivity of a global database of ICSRs to known pharmacogenomic risk variants common in Japan. METHODS: The data source was VigiBase, the global database of ICSRs, including all reports entered in the version frozen on 5 January 2020. Subgroup disproportionality analysis was used to compare ICSRs of two subgroups, Japan and rest of world (RoW). Reports for UGT1A1-metabolized irinotecan and the CYP2C19-metabolized drugs voriconazole, escitalopram and clopidogrel were selected for comparison between the subgroups based upon known genetic polymorphisms with high prevalence in Japan. Contrast between the subgroups was quantified by IC delta [Formula: see text]), a robust shrinkage observed-to-expected (OE) ratio on a log scale. Harmonic mean p values (HMP) were calculated for each drug to evaluate whether a list of pre-specified ADRs were collectively significantly over- (or under-)reported as hypothesized. Daily drug dosages were calculated for ICSRs with sufficient information, and dose distributions were compared between Japan and RoW and related to differences in regionally approved doses. RESULTS: The predictions of over-reporting patterns for specific ADRs were observed and confirmed in bootstrap HMP analyses (p = 0.004 for irinotecan and p < 0.001 for each of voriconazole, escitalopram and clopidogrel) and compared with similar drugs with different metabolic pathways. The impact of proactive regulatory action, such as recommended dosing and therapeutic drug monitoring (TDM), was also observable within the global database. For irinotecan and escitalopram, there was evidence of use of lower dosages as recommended in the Japanese labels; for voriconazole, there was evidence of use of TDM with an over-reporting of terms related to drug level measurements and an under-reporting of liver toxicity. CONCLUSIONS: Pharmaco-ethnic vulnerabilities caused by pharmacogenomic differences between populations may contribute to differences in ADR reporting between countries in a global database of ICSRs. Regional analyses within a global database can inform on the effectiveness of local risk minimization measures and should be leveraged to catalyse the conversion of real-world usage into safer use of drugs in ethnically tailored ways.

Revisiting Nonlinear Bosentan Pharmacokinetics by Physiologically Based Pharmacokinetic Modeling: Target Binding, Albeit Not a Major Contributor to Nonlinearity, Can Offer Prediction of Target Occupancy.

Bosentan is a high-affinity antagonist of endothelin receptors and one of the earliest examples for target-mediated drug disposition [a type of nonlinear pharmacokinetics (PKs) caused by saturable target binding]. The previous physiologically based PK (PBPK) modeling indicated that the nonlinear PKs of bosentan was explainable by considering saturable hepatic uptake. However, it remained unexamined to what extent the saturable target binding contributes to the nonlinear PKs of bosentan. Here, we developed a PBPK model incorporating saturable target binding and hepatic uptake and analyzed the clinical bosentan PK data using the cluster Gauss-Newton method (CGNM). The PBPK model without target binding fell short in capturing the bosentan concentrations below 100 nM, based on the PK profiles and the goodness-of-fit plot. Both global and local identifiability analyses (using the CGNM and Fisher information matrix, respectively) informed that the target binding parameters were identifiable only if the observations from the lowest dose (10 mg) were included. By analyzing blood PK profiles alone, the PBPK model with target binding yielded practically identifiable target binding parameters and predicted the maximum target occupancies of 0.6-0.8 at clinical bosentan doses. Our results indicate that target binding, albeit not a major contributor to the nonlinear bosentan PKs, may offer a prediction of target occupancy from blood PK profiles alone and potential guidance on achieving optimal efficacy outcomes, under the condition when the high-affinity drug target is responsible for the efficacy of interest and when the dose ranges cover varying degrees of target binding. SIGNIFICANCE STATEMENT: By incorporating saturable target binding, our physiologically based pharmacokinetic (PBPK) model predicted in vivo target occupancy of bosentan based only on the blood concentration-time profiles obtained from a wide range of doses. Our analysis highlights the potential utility of PBPK models that incorporate target binding in predicting target occupancy in vivo.

Normalized power variance of eLORETA at high-convexity area predicts shunt response in idiopathic normal pressure hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is a neuropsychiatric disease characterized by gait disturbance, cognitive deterioration and urinary incontinence associated with excessive accumulation of cerebrospinal fluid (CSF) in the brain ventricles. These symptoms, in particular gait disturbance, can be potentially improved by shunt operation in the early stage of the disease, and the intervention associates with a worse outcome when performed late during the course of the disease. Despite the variable outcome of shunt operation, noninvasive presurgical prediction methods of shunt response have not been established yet. In the present study, we used normalized power variance (NPV), a sensitive measure of the instability of cortical electrical activity, to analyze cortical electrical activity derived from EEG data using exact-low-resolution-electromagnetic-tomography (eLORETA) in 15 shunt responders and 19 non-responders. We found that shunt responders showed significantly higher NPV values at high-convexity areas in beta frequency band than non-responders. In addition, using this difference, we could discriminate shunt responders from non-responders with leave-one-subject-out cross-validation accuracy of 67.6% (23/34) [positive predictive value of 61.1% (11/18) and negative predictive value of 75.0% (12/16)]. Our findings indicate that eLORETA-NPV can be a useful tool for noninvasive prediction of clinical response to shunt operation in patients with iNPH.

Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models.

Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.

Risk Factor Considerations in Statistical Signal Detection: Using Subgroup Disproportionality to Uncover Risk Groups for Adverse Drug Reactions in VigiBase.

INTRODUCTION: In the treatment of the individual patient, a vision is to achieve the best possible balance between benefit and harm. Such tailored therapy relies upon the identification and characterisation of risk factors for adverse drug reactions. Information relevant to risk factor considerations can be captured in adverse event reports and could be utilised in statistical signal detection. OBJECTIVE: The aim of this study was to explore whether statistical screening of a broad range of risk factors within a global database of adverse event reports could uncover signals of risk groups for adverse drug reactions. METHODS: Subgroup disproportionality analysis was applied to 15.4 million reports entered in VigiBase, the World Health Organization (WHO) global database of individual case safety reports, up to August 2017. Disproportionality analyses for drug-adverse event pairs were performed (1) in the full database and (2) across a range of subgroups defined by the following covariates: patient age, sex, body mass index, pregnancy, underlying condition, reporting country, and geographical region. Drug-adverse event pairs disproportionately over-reported in such subgroups, but not in the full database, and with a substantial difference between the two observed-to-expected ratios, were highlighted as statistical signals. These were further prioritised, through filtering and sorting, for clinical assessment, whereafter clinically relevant signals were communicated to the pharmacovigilance community and the public. RESULTS: Assessments were performed for 354 prioritised statistical signals, resulting in seven communicated signals describing previously unrecognised potential risk groups related to age (elderly), sex (male and female), body mass index (underweight and obese), and geographical region (Asia), all except one for already established adverse drug reactions. Important aspects considered in the assessments included an evaluation of the disproportionate over-reporting in the subgroup by reviewing alternative explanations and reporting patterns for similar drugs/adverse events/subgroups, and a search for plausible mechanisms to support the risk hypothesis. CONCLUSIONS: This study reveals that it is possible to uncover signals of risk groups for adverse drug reactions through incorporation of broad risk factor screening into statistical signal detection in a global database of adverse event reports. Our findings suggest the potential to use such statistical methodologies for risk characterisation in subpopulations of concern.

Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations.

INTRODUCTION: Uncovering safety signals through the collection and assessment of individual case reports remains a core pharmacovigilance activity. Despite the widespread use of disproportionality analysis in signal detection, recommendations are lacking on the minimum size of databases or subsets of databases required to yield robust results. OBJECTIVE: This study aims to investigate the relationship between database size and robustness of disproportionality analysis, with regards to limiting spurious associations. METHODS: Three types of subsets were created from the global database VigiBase: random subsets (500 replicates each of 11 fixed subset sizes between 250 and 100,000 reports), country-specific subsets (all 131 countries available in the original VigiBase extract) and subsets based on the Anatomical Therapeutic Chemical classification. For each subset, a spuriousness rate was computed as the ratio between the number of drug-event combinations highlighted by disproportionality analysis in a permuted version of the subset and the corresponding number in the original subset. In the permuted data, all true reporting associations between drugs and adverse events were broken. Subsets with fewer than five original associations were excluded. Additionally, the set of disproportionately over-reported drug-event combinations in three specific countries at three different time points were clinically assessed for labelledness. These time points corresponded to database sizes of less than 10,000, 5000 and 1000 reports, respectively. All disproportionality analysis was based on the Information Component (IC), implemented as IC025 > 0. RESULTS: Spuriousness rates were below 0.15 for all 110 included countries regardless of subset size, with only seven countries (6%) exceeding the empirical threshold of 0.10 observed for large subsets. All 21 excluded countries had < 500 reports. For random subsets containing 3000-5000 or more reports, the higher end of observed spuriousness rates was close to 0.10. In the clinical assessment, the proportion of labelled or otherwise known drug-event combinations was very high (87-100%) across all countries and time points studied. CONCLUSIONS: To mitigate the risk of highlighting spurious associations with disproportionality analysis, a minimum size of 500 reports is recommended for national databases. For databases or subsets that are not country-specific, our recommendation is 5000 reports. This study does not consider sensitivity, which is expected to be poor in smaller databases.