Prediction Of Clearance Research Articles

Prevalence and prediction of augmented renal clearance in the neurocritical care population

Background: Augmented renal clearance (ARC; creatinine clearance [CrCl] >130 mL/min/1.73 m2) is prevalent in patients with neurological injuries and may influence their exposure to important pharmacological therapies. Little is known about the relationship between estimated and measured CrCl in this population.Methods: This single-center, prospective, observational cohort study aimed to describe the association between ARC and estimated CrCl and neurological outcomes in a broad neurocritical care population. Prospective patient screening criteria included adults aged 18–85 years, with critical illness due to neurologic causes (such as ischemic stroke or subarachnoid hemorrhage) and lack of renal dysfunction on admission. Patients who had at least one urine CrCl measurement performed within the first 7 days of hospitalization were included. Two cohorts were evaluated: those with ARC and those without ARC. Results: Fifty-seven patients were included, of whom 49 (86%) exhibited ARC. Subjects with ARC were more likely to be male and had a significantly higher median measured CrCl (201.7 mL/min/1.73 m2) than those without ARC (109.8 mL/min/1.73 m2). The Augmented Renal Clearance in Trauma Intensive Care (ARCTIC) score displayed the strongest association (vs. CrCl equations) with ARC development (area under the receiver operating characteristic curve, 0.648). Conclusion: The prevalence of ARC in the present study of a broad neurocritical care population appeared to be high (86%). The ARCTIC score had higher sensitivity and specificity for diagnosing ARC than the common serum creatinine-based estimation.

In vitro metabolism of the new antifungal dapaconazole using liver microsomes

Dapaconazole is a new antifungal imidazole that has been shown a high efficacy against several pathogenic fungi. This study aimed to investigate the interspecies variation in the in vitro metabolic profiles and in vivo hepatic clearance (CLH,in vivo) prediction of dapaconazole using liver microsomes from male Sprague Dawley rat, male Beagle dog and mixed gender human using a liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) method. In addition, the produced metabolites were identified by ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometer (UHPLC-QTOF-MS/MS). The microsomal protein concentration of 0.1 mg/mL and the incubation time of 10 min were employed for the kinetics determination, resulting in a sigmoidal kinetic profile for all species evaluated. The predicted CLH,in vivo was 6.5, 11.6 and 7.5 mL/min/kg for human, rat and dog, respectively. Furthermore, five metabolized products were identified. These findings provide preliminary information for understanding dapaconazole metabolism and the interspecies differences in catalytic behaviours, supporting the choice of a suitable laboratory animal for future pharmacokinetics and metabolism studies.

Improved Predictability of Hepatic Clearance with Optimal pH for Acyl-Glucuronidation in Liver Microsomes

The purpose of this study was to investigate the optimal pH for acyl-glucuronidation formation with carboxylic acid-containing compounds in human and rat liver microsomes to improve the predictability of their hepatic clearance. The optimal pH for acyl-glucuronidation of all 17 compounds was around pH 6.0 in human and rat liver microsomes. Correlation analysis was done with the predicted in vitro intrinsic clearance (CLint,in vitro) and in vivo intrinsic clearance (CLint,in vivo) calculated from available reported data of total clearance (CLtot) of 11 compounds in humans. For 8 of the 11 compounds, under the pH 6.0 condition, the CLint,in vitro were within 1/3 to 3-fold error of the observed CLint,in vivo whereas, the error was within 1/3 to 3-fold of the observed CLint,in vivo for only 3 of the 11 under the pH 7.4 condition. The intracellular pH in human and rat hepatocytes decreased in the presence of a carboxylic acid-containing compound. These findings suggest that acyl-glucuronidation in liver microsomes at pH 6.0 is closer to physiological conditions in the presence of carboxylic acid compounds, and thus, use of this pH condition is important for physiological interpretation and predictability of intrinsic clearance.

Evidence of the need for modified well-stirred model in vitro to in vivo extrapolation

In vitro to in vivo extrapolation (IVIVE), an approach for hepatic clearance (CLH) prediction used worldwide, remains controversial due to systematic underprediction. Among the various probable factors, the original assumption of the hepatic mathematical model (i.e., the well-stirred model, WSM) may become problematic, leading to the underestimation of drug CLH. Having a similar prerequisite that the well-stirred conditions are homogenous with perfectly mixed reactants, but using a different driving concentration, the modified well-stirred model (MWSM) stands apart from the WSM. However, we believe that both models should coexist so that the entire well-stirred scenario can be completely illustrated. Consequently, we collected published data from the literature and employed a logistic regression method to differentiate the optimal timing of use between WSM and MWSM in drug CLH prediction. Generally, variances adopted in the regression, including partition coefficient (logP), fraction unbound (fu), volumes of distribution at steady-state (Vss), and mean residence time (MRT), corresponded to our assumption when protein-facilitated uptake was considered. Furthermore, a new empirical approach was introduced to allow practical use of the MWSM. The results showed that this model could provide a more precise prediction compared to previous empirical approaches. Therefore, these preliminary results not only delineated a more detailed structure and mechanism of MWSM but also highlighted its necessity and potential.

Utilizing virtual experiments to increase understanding of discrepancies involving in vitro-to-in vivo predictions of hepatic clearance.

Predictions of xenobiotic hepatic clearance in humans using in vitro-to-in vivo extrapolation methods are frequently inaccurate and problematic. Multiple strategies are being pursued to disentangle responsible mechanisms. The objective of this work is to evaluate the feasibility of using insights gained from independent virtual experiments on two model systems to begin unraveling responsible mechanisms. The virtual culture is a software analog of hepatocytes in vitro, and the virtual human maps to hepatocytes within a liver within an idealized model human. Mobile objects (virtual compounds) map to amounts of xenobiotics. Earlier versions of the two systems achieved quantitative validation targets for intrinsic clearance (virtual culture) and hepatic clearance (virtual human). The major difference between the two systems is the spatial organization of the virtual hepatocytes. For each pair of experiments (virtual culture, virtual human), hepatocytes are configured the same. Probabilistic rules govern virtual compound movements and interactions with other objects. We focus on highly permeable virtual compounds and fix their extracellular unbound fraction at one of seven values (0.05–1.0). Hepatocytes contain objects that can bind and remove compounds, analogous to metabolism. We require that, for a subset of compound properties, per-hepatocyte compound exposure and removal rates during culture experiments directly predict corresponding measures made during virtual human experiments. That requirement serves as a cross-system validation target; we identify compound properties that enable achieving it. We then change compound properties, ceteris paribus, and provide model mechanism-based explanations for when and why measures made during culture experiments under- (or over-) predict corresponding measures made during virtual human experiments. The results show that, from the perspective of compound removal, the organization of hepatocytes within virtual livers is more efficient than within cultures, and the greater the efficiency difference, the larger the underprediction. That relationship is noteworthy because most in vitro-to-in vivo extrapolation methods abstract away the structural organization of hepatocytes within a liver. More work is needed on multiple fronts, including the study of an expanded variety of virtual compound properties. Nevertheless, the results support the feasibility of the approach and plan.

Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey.

Accurate prediction of human clearance (CL) and volume of distribution at steady state (Vd,ss) for small molecule drug candidates is an essential component of assessing likely efficacious dose and clinical safety margins. In 2021, the IQ Consortium Human PK Prediction Working Group undertook a survey of IQ member companies to understand the current PK prediction methods being used to estimate these parameters across the pharmaceutical industry. The survey revealed a heterogeneity in approaches being used across the industry (e.g., the use of allometric approaches, differing incorporation of binding terms, and inconsistent use of empirical correction factors for in vitro-in vivo extrapolation, IVIVE), which could lead to different PK predictions with the same input data. Member companies expressed an interest in improving human PK predictions by identifying the most appropriate compound-class specific methods, as determined by physiochemical properties and knowledge of CL pathways. Furthermore, there was consensus that increased understanding of the uncertainty inherent to the compound class-dependent prediction would be invaluable in aiding communication of human PK and dose uncertainty at the time of candidate nomination for development. The human PK Prediction Working Group is utilizing these survey findings to help interrogate clinical IV datasets from across the IQ consortium member companies to understand PK prediction accuracy and uncertainty from preclinical datasets.

Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs.

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (β) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.

Validation of the Augmented Renal Clearance in Trauma Intensive Care scoring system for augmented renal clearance prediction in a trauma subgroup of a mixed ICU population

Augmented renal clearance is prevalent in trauma patients and leads to subtherapeutic levels of renally eliminated medications with potentially unfavourable clinical outcomes. The Augmented Renal Clearance of Trauma in Intensive Care (ARCTIC) score has been developed to predict augmented renal clearance in critically ill trauma patients. Our primary objective was to validate this score among the trauma subgroup of a mixed intensive care patient cohort. This single-centre, retrospective, observational cohort study assessed augmented renal clearance using a timed 24-h urine collection performed weekly. ARC was defined as a measured creatinine clearance of ≥130 ml/min/1.73 m2 . ARCTIC score performance was evaluated through a receiver operator characteristic curves and analysis of sensitivities and specificities for the trauma subgroup, the medical/surgical subgroup and the pooled cohort. Augmented renal clearance was observed in 33.9% (n=58) of trauma patients (n=171) and 15.7% (n=24) of medical/surgical patients (n=153). Examination of different cutoffs for the ARCTIC score in our trauma population confirmed that the optimal cutoff score was ≥6. Comparison between ROC curves for ARCTIC score and for regression model based upon our data in trauma patients indicated validation of the score in this subgroup. Comparison of sensitivities and specificities for ARCTIC score between trauma (93.1% and 41.6%, respectively) and medical/surgical subjects (87.5% and 49.6%, respectively) showed no clinical nor statistical difference, suggesting validation for the medical/surgical subgroup as well. In our mixed ICU population, the ARCTIC score was validated in the trauma subgroup. We also found that the score performed well in the medical/surgical population. Future studies should assess the performance of the ARCTIC score prospectively.

Identification and Strategies to Mitigate High Total Clearance of Benzylamine-Substituted Biphenyl Ring Systems.

For most oral small-molecule projects within drug discovery, the extent and duration of the effect are influenced by the total clearance of the compound; hence, designing compounds with low clearance remains a key focus to help enable sufficient protein target engagement. Comprehensive understanding and accurate prediction of animal clearance and pharmacokinetics provides confidence that the same can be observed for human. During a MERTK inhibitor lead optimization project, a series containing a biphenyl ring system with benzylamine meta-substitution on one phenyl and nitrogen inclusion as the meta atom on the other ring demonstrated multiple routes of compound elimination in rats. Here, we describe the identification of a structural pharmacophore involving two key interactions observed for both the MERTK program and an additional internal project. Four strategies to mitigate these clearance liabilities were identified and systematically investigated. We provide evidence that disruption of at least one of the interactions led to a significant reduction in CL that was subsequently predicted from rat hepatocytes using in vitro/in vivo extrapolation and the well-stirred scaling method. These tactics will likely be of general utility to the medicinal chemistry and DMPK community during compound optimization when similar issues are encountered for biphenyl benzylamines.

In-vitro NET-osis induced by COVID-19 sera is associated to severe clinical course in not vaccinated patients and immune-dysregulation in breakthrough infection

Since neutrophil extracellular traps formation (NET-osis) can be assessed indirectly by treating healthy neutrophils with blood-derived fluids from patients and then measuring the NETs response, we designed a pilot study to convey high-dimensional cytometry of peripheral blood immune cells and cytokines, combined with clinical features, to understand if NET-osis assessment could be included in the immune risk profiling to early prediction of clinical patterns, disease severity, and viral clearance at 28 days in COVID-19 patients. Immune cells composition of peripheral blood, cytokines concentration and in-vitro NETosis were detected in peripheral blood of 41 consecutive COVID-19 inpatients, including 21 mild breakthrough infections compared to 20 healthy donors, matched for sex and age. Major immune dysregulation in peripheral blood in not-vaccinated COVID-19 patients compared to healthy subjects included: a significant reduction of percentage of unswitched memory B-cells and transitional B-cells; loss of naïve CD3+CD4+CD45RA+ and CD3+CD8+CD45RA+ cells, increase of IL-1β, IL-17A and IFN-γ. Myeloid compartment was affected as well, due to the increase of classical (CD14++CD16−) and intermediate (CD14++CD16+) monocytes, overexpressing the activation marker CD64, negatively associated to the absolute counts of CD8+ CD45R0+ cells, IFN-γ and IL-6, and expansion of monocytic-like myeloid derived suppressor cells. In not-vaccinated patients who achieved viral clearance by 28 days we found at hospital admission lower absolute counts of effector cells, namely CD8+T cells, CD4+ T-cells and CD4+CD45RO+ T cells. Percentage of in-vitro NET-osis induced by patients’ sera and NET-osis density were progressively higher in moderate and severe COVID-19 patients than in mild disease and controls. The percentage of in-vitro induced NET-osis was positively associated to circulating cytokines IL-1β, IFN-γ and IL-6. In breakthrough COVID-19 infections, characterized by mild clinical course, we observed increased percentage of in-vitro NET-osis, higher CD4+ CD45RO+ and CD8+ CD45RO+ T cells healthy or mild-COVID-19 not-vaccinated patients, reduced by 24 h of treatment with ACE inhibitor ramipril. Taken together our data highlight the role of NETs in orchestrating the complex immune response to SARS-COV-2, that should be considered in a multi-target approach for COVID-19 treatment.

Development and Use of an Ex-Vivo In-Vivo Correlation to Predict Antiepileptic Drug Clearance in Patients Undergoing Continuous Renal Replacement Therapy.

Results from previous ex-vivo continuous renal replacement therapy (CRRT) models have successfully demonstrated similar extraction coefficients (EC) identified from in-vivo clinical trials. The objectives of this study are to develop an ex-vivo in-vivo correlation (EVIVC) model to predict drug clearance for commonly used antiepileptics and to evaluate similarity in drug extraction across different CRRT modalities to extrapolate dosing recommendations. Levetiracetam, lacosamide, and phenytoin CRRT clearance was evaluated using the Prismaflex CRRT system and M150 hemodiafilters using an albumin containing normal saline (ALB-NS) vehicle with 3 different albumin concentrations (2g/dL, 3g/dL, and 4g/dL) and a human plasma vehicle at 3 different effluent flow rates (1 L/hr, 2 L/hr, and 3 L/hr). Blood and effluent/dialysate concentrations were collected after circuit priming. ECs were calculated for each drug, modality, vehicle, and experimental arm combination. The calculated average EC for levetiracetam and lacosamide was approximated to the fraction unbound from plasma protein. Human plasma and ALB-NS vehicles demonstrated adequate prediction of in-vivo CRRT clearance. Geometric mean ratios indicated similarity in extraction coefficients when comparing between hemofiltration and hemodiafiltration modalities and between filtration and dialysis modalities at effluent flow rates ≤ 2L/hr. Evaluation of phenytoin provided inconsistent findings with regards to extraction coefficient similarity across different CRRT modalities. The findings indicate that an ex-vivo study can be used as a surrogate to predict in-vivo levetiracetam and lacosamide clearance in patients receiving CRRT.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up” approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure.

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.

Cytochrome P450 isoforms contribution, plasma protein binding, toxicokinetics of enniatin A in rats and in vivo clearance prediction in humans

Emerging mycotoxins, such as enniatin A (ENNA), are becoming a worldwide concern owing to their presence in different types of food and feed. However, comprehensive toxicokinetic data that links intake, exposure and toxicological effects of ENNA has not been elucidated yet. Therefore, the present study investigated the in vitro (rat and human) and in vivo (rat) toxicokinetic properties of ENNA. Towards this, an easily applicable and sensitive bioanalytical method was developed and validated for the estimation of ENNA in rat plasma. ENNA exhibited high plasma protein binding (99%), high hepatic clearance and mainly underwent metabolism via CYP3A4 (74%). The in-house predicted hepatic clearance (54 mL/min/kg) and observed in vivo rat clearance (55 mL/min/kg) were comparable. The predicted in vivo human hepatic clearance was 18 mL/min/kg. ENNA underwent slow absorption (Tmax = 4 h) and rapid elimination following oral administration to rats. The absolute oral bioavailability was 47%. The toxicokinetic findings for ENNA from this study will help in designing and interpreting toxicological studies in rats. Besides, these findings could be used in physiologically based toxicokinetic (PBTK) model development for exposure predictions and risk assessment for ENNA in humans.

Advantages of Allometric Scaling Methods for Predicting Human Pharmacokinetics of Novel JAK Inhibitor -Baricitinib and Dose Extrapolation

Purpose: The primary motive of this study was to examine advantages of allometry scaling strategies for correct prediction of pharmacokinetics of Baricitinib in human from preclinical species. Baricitinib is basically Janus kinase (JAK) inhibitor used for the treatment of rheumatoid arthritis. Currently approved by FDA in combination with remdesivir for treatment of COVID-19 hospitalized patient. Methods: The literature published pharmacokinetic parameters (Cl and Vd) of preclinical species (Rat, Dog and monkey) were utilized for the allometry scaling of Baricitinib. The connection among the primary pharmacokinetic parameters [Volume of distribution (Vd) and clearance (Cl)] and body weight (BW) were studied across three preclinical species, we used the double logarithmic plots for prediction of the human pharmacokinetic parameters i.e. Cl and Vd with use of simple allometry and with additional correction factors for better prediction. The dose extrapolation of baricitinib was carried out by FDA guidelines. Results: By application of the allometric scaling methods and principles correlation was found to be satisfactory for the prediction of intravenous human Cl and Vd for baricitinib. The volume of distribution (Vd) predicted by simple allometry (65.3 L) was found to be in agreement with the reported value (75.5 L); clearance (Cl) prediction by simple allometry was found to be at least 1.06 -closer to the reported value (245 mL/min); CF were used to predict the clearance. Both brain weight (B.W) and maximum life span potential (MLP) predicted the Cl with 0.52- and 0.61 -fold difference. The application of monkey liver blood flow predicted Cl with 0.81 fold which was also in close agreement with reported value. The Cl prediction was also extrapolated using LBF (Liver blood flow) method and observed that the higher species (Dog and Monkey) have predicted Cl with better accuracy than rat. Conclusions: Overall, the simple allometry (SA), monkey liver blood flow (MLBF) and application of liver blood flow (LBF) methods showed excellent correlation with human. The time vs. plasma concentration simulated graph also showed the similar closeness with human profile. The inclusion of plasma protein binding factor didn’t improve the prediction accuracy. The FIH dose extrapolation were showed that PK guided approach and exponent for BSA based approach was found closer to actual human dose of 4.0 mg/Kg.

Prediction of Hepatobiliary Clearances and Hepatic Concentrations of Transported Drugs in Humans Using Rosuvastatin as a Model Drug.

To assess efficacy and toxicity of a drug in humans, it is important to measure the tissue concentration of a drug at the target site. For a drug that is transported into or out of the tissue, the tissue unbound steady-state concentration can be dramatically different from its corresponding unbound steady-state plasma concentration. Because routine measurement of drug tissue concentrations is not possible, using rosuvastatin as a model transporter substrate drug, we compared the ability of the proteomics-informed relative expression factor (REF) approach and sandwich-cultured human hepatocytes (SCH) to accurately predict rosuvastatin human hepatobiliary clearances and hepatic concentrations. REF-predicted rosuvastatin biliary clearance (CLbile ), estimated using BCRP-overexpressing, MDR1-overexpressing, and MRP2-overexpressing vesicles, together with our previously published REF-predicted rosuvastatin hepatic sinusoidal uptake clearance (CLuptake ) and physiologically scaled sinusoidal passive uptake and efflux clearance (CLs,efflux ), were used to predict rosuvastatin hepatic concentrations. For SCH, the estimated rosuvastatin CLbile , CLuptake , and CLs,efflux were scaled using physiological scaling. The REF-predicted CLbile (6.39±1.56mL/minute) and hepatic rosuvastatin area under the concentration-time curve (AUC) fell within our a priori defined success criterion, i.e., within twofold of the observed positron emission tomography-imaged values. In contrast, as expected, SCH dramatically overpredicted (predicted/observed ratio P/O=8.38-10.41) rosuvastatin CLbile , and underpredicted hepatic AUC (P/O=0.08-0.14). For both approaches, predictions were improved by using the parallel tube model vs. well-stirred model. Overall, using rosuvastatin as a model drug, this study demonstrates the success of the REF approach in predicting in vivo CLbile and hepatic concentration of drugs, and highlights the shortcomings of the SCH approach in making such predictions.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species.

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Multi-Well Array Culture of Primary Human Hepatocyte Spheroids for Clearance Extrapolation of Slowly Metabolized Compounds.

Accurate prediction of human pharmacokinetics using in vitro tools is an important task during drug development. Albeit, currently used in vitro systems for clearance extrapolation such as microsomes and primary human hepatocytes in suspension culture show reproducible turnover, the utility of these systems is limited by a rapid decline of activity of drug metabolizing enzymes. In this study, a multi-well array culture of primary human hepatocyte spheroids was compared to suspension and single spheroid cultures from the same donor. Multi-well spheroids remained viable and functional over the incubation time of 3 days, showing physiological excretion of albumin and α-AGP. Their metabolic activity was similar compared to suspension and single spheroid cultures. This physiological activity, the high cell concentration, and the prolonged incubation time resulted in significant turnover of all tested low clearance compounds (n = 8). In stark contrast, only one or none of the compounds showed significant turnover when single spheroid or suspension cultures were used. Using multi-well spheroids and a regression offset approach (log(CLint) = 1.1 × + 0.85), clearance was predicted within 3-fold for 93% (13/14) of the tested compounds. Thus, multi-well spheroids represent a novel and valuable addition to the ADME in vitro tool kit for the determination of low clearance and overall clearance prediction. Graphical Abstract.

Cystatin C and Fibrinogen Plasma Levels as early Predictors of Diabetic Nephropathy in Type II Diabetes Mellitus; a Review Article

Background: Diabetic nephropathy (DN) is a dangerous illness associated with a significant risk for cardiovascular and kidney problems in diabetic patients. Serum cystatin C levels may rise in diabetic individuals with microalbuminuria, and it has been recommended as an endogenous glomerular filtration rate (GFR) marker because of its link to the albumin to creatinine ratio (ACR) in diabetic nephropathy. Uncontrolled diabetes was found to have a greater level of fibrinogen; in diabetic nephropathy, fibrinogen levels are important. In addition, fibrinogen has been associated to inflammation and has been demonstrated to play a crucial pathophysiologic role in the advancement of renal impairment in individuals. Methods: The author has no intention of commenting on the molecular role of cystatin C, a cysteine protease inhibitor, or the disrupted haemostatics mechanism in fibrinogen-induced diabetes. in this study, which is expected to investigate views on using the cystatin C as well as plasma fibrinogen plasma levels like an early markers of nephrotic syndrome. Therefore, 4 important clinical datasets were reviewed, including the EMBASE, PubMed and The Cochrane Library, Medline, Google Scholar and a few additional related journals datasets, as well as relevant records were collected with high precision.‎ Conclusion: When compared to the frequently used creatinine-based predictions, cystatin C is a good marker for diagnosing nephropathy in patients with normal albuminuria, and it may enhance the risk prediction in diabetics. Even before the complication of chronic kidney disease symptoms, Cystatin C levels in urine might be raised in diabetic individuals. Additional investigation into cystatin C and fibrinogen functions as early biomarkers, clinical value in screening, involvement in prognosis, decrease of inflammation and prediction of medication clearance, and drug monitoring in type II diabetic, nephropathy is needed.‎ Keywords: cystatin C, fibrinogen, nephropathy, diabetes mellitus, biomarkers

Does Addition of Protein to Hepatocyte or Microsomal In Vitro Incubations Provide a Useful Improvement in In Vitro-In Vivo Extrapolation Predictability?

Accurate prediction of in vivo hepatic clearance is an essential part of successful and efficient drug development; however, many investigators have recognized that there are significant limitations in the predictability of clearance with a tendency for underprediction for primarily metabolized drugs. Here, we examine the impact of adding serum or albumin into hepatocyte and microsomal incubations on the predictability of in vivo hepatic clearance. The addition of protein into hepatocyte incubations has been reported to improve the predictability for high clearance (extraction ratio) drugs and highly protein-bound drugs. Analyzing published data for 60 different drugs and 97 experimental comparisons (with 17 drugs being investigated from two to seven) we confirmed the marked underprediction of clearance. However, we could not validate any relevant improved predictability within twofold by the addition of serum to hepatocyte incubations or albumin to microsomal incubations. This was the case when investigating all measurements, or when subdividing analyses by extraction ratio, degree of protein binding, Biopharmaceutics Drug Disposition Classification System class, examining Extended Clearance Classification System class 1B drugs only, or drug charge. Manipulating characteristics of small data sets of like compounds and adding scaling factors can appear to yield good predictability, but the carryover of these methods to alternate drug classes and different laboratories is not evident. Improvement in predictability of poorly soluble compounds is greater than that for soluble compounds, but not to a meaningful extent. Overall, we cannot confirm that protein addition improves in vitro-in vivo extrapolation predictability to any clinically meaningful degree when considering all drugs and different subsets. SIGNIFICANCE STATEMENT: The addition of protein into microsomal or hepatocyte incubations has been widely proposed to improve hepatic clearance predictions. To date, studies examining this phenomenon have not included appropriate negative controls where predictability is achieved without protein addition and have been conducted with small data sets of similar compounds that don't apply to alternate drug classes. Here, an extensive analysis of published data for 60 drugs and 97 experimental comparisons couldn't validate any relevant clinically improved clearance predictability with protein addition.