Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®

BACKGROUND AND OBJECTIVE

In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software.

METHODS

A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included.

RESULTS

The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range.

CONCLUSIONS

Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.

New insights into the role of VKORC1 polymorphisms for optimal warfarin dose selection in Caribbean Hispanic patients through an external validation of a population PK/PD model

Warfarin, an oral anticoagulant, has been used for decades to prevent thromboembolic events. The complex interplay between CYP2C9 and VKORC1 genotypes on warfarin PK and PD properties is not fully understood in special sub-groups of patients. This study aimed to externally validate a population pharmacokinetic/pharmacodynamic (PK/PD) model for the effect of warfarin on international normalized ratio (INR) and to evaluate optimal dosing strategies based on the selected covariates in Caribbean Hispanic patients. INR, and CYP2C9 and VKORC1 genotypes from 138 patients were used to develop a population PK/PD model in NONMEM. The structural definition of a previously published PD model for INR was implemented. A numerical evaluation of the parameter-covariate relationship was performed. Simulations were conducted to determine optimal dosing strategies for each genotype combinations, focusing on achieving therapeutic INR levels. Findings revealed elevated IC50 for G/G, G/A, and A/A VKORC1 haplotypes (11.76, 10.49, and 9.22 mg/L, respectively), in this population compared to previous reports. The model-guided dosing analysis recommended daily warfarin doses of 3-5 mg for most genotypes to maintain desired INR levels, although subjects with combination of CYP2C9 and VKORC1 genotypes * 2/* 2-, * 2/* 3- and * 2/* 5-A/A would require only 1 mg daily. This research underscores the potential of population PK/PD modeling to inform personalized warfarin dosing in populations typically underrepresented in clinical studies, potentially leading to improved treatment outcomes and patient safety. By integrating genetic factors and clinical data, this approach could pave the way for more effective and tailored anticoagulation therapy in diverse patient groups.

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment

Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8-11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10-9), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.

Application of population physiologically based pharmacokinetic modelling to optimize target expression and clearance mechanisms of therapeutic monoclonal antibodies

AIMS

To use population physiologically based pharmacokinetic (PopPBPK) modelling to optimize target expression, kinetics and clearance of HER1/2 directed therapeutic monoclonal antibodies (mAbs). Thus, to propose a general workflow of PopPBPK modelling and its application in clinical pharmacology.

METHODS

Full PBPK model of pertuzumab (PTZ) was developed in patient population using Simcyp V21R1 incorporating mechanistic targeted-mediated drug disposition process by fitting known clinical PK and sparse receptor proteomics data to optimize target expression and kinetics of HER2 receptor. Trastuzumab (TTZ) PBPK modelling was used to validate the optimized HER2 target. Additionally, the simulator was also used to develop a full PBPK model for the HER1-directed mAb cetuximab (CTX) to assess the underlying targeted-mediated drug disposition-independent elimination mechanisms.

RESULTS

HER2 final parameterisation coming from the PBPK modelling of PTZ was successfully cross validated through PBPK modelling of TTZ with average fold error (AFE), absolute AFE and percent prediction error values for area under the concentration-time curve (AUC) and maximum plasma concentration (Cmax ) of 1.13, 1.16 and 16, and 1.01, 1.07 and 7, respectively. CTX PBPK model performance was validated after the incorporation of an additional systemic clearance of 0.033 L/h as AFE and absolute AFE showed an acceptable predictive power of AUC and Cmax with percent prediction error of 13% for AUC and 10% for Cmax .

CONCLUSIONS

Optimisation of both system and drug related parameters were performed through PBPK modelling to improve model performance of therapeutic mAbs (PTZ, TTZ and CTX). General workflow was proposed to develop and apply PopPBPK to support clinical development of mAbs targeting same receptor.

Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles

BACKGROUND AND OBJECTIVES

The most widely used method to compare dissolution profiles is the similarity factor f₂. When this method is not applicable, the confidence interval of f₂ using bootstrap methodology has been recommended instead. As neither details of the estimator nor the types of confidence intervals are described in the guidelines, the suitability of five estimators and fourteen types of confidence intervals were investigated in this study by simulation.

METHODS

One million individual dissolution profiles were simulated for the reference and test populations with predefined target population f₂ values, where random samples of different sizes were drawn without replacement. From each pair of random samples, five f₂ estimators were calculated, and fourteen types of confidence intervals were obtained using 5000 bootstrap samples. The whole process was repeated 10000 times and the percentage of the similarity conclusions was measured. In addition, the uncertainty associated with the current practice of using f^₂ point estimate alone for the statistical inference was evaluated.

RESULTS

When combined with different types of confidence intervals, the estimated f₂ (f^₂), the bias-corrected f₂ (f^_2,bc), and the variance- and bias-corrected f₂ (f^_2,vcbc) are not suitable estimators due to higher-than-acceptable type I errors. The estimator f^_2,exp, calculated based on the mathematical expectation of f^₂, and f^_2,vcexp, the variance-corrected f^_2,exp, showed acceptable type I errors when combined with any of the ten percentile intervals. However, they have the drawback of low power, which might be addressed by increasing the sample size. To properly control the type I error, samples with at least 12 units should be used.

CONCLUSION

The best combinations of estimator and type of confidence interval are f^_2,exp and f^_2,vcexp combined with any of the ten types of percentile intervals. When the sample f₂ value is close to 50, the use of the confidence interval of f₂ is recommended even when the variability of the dissolution profiles is low and the prerequisites defined in the regulatory guidelines for using the conventional f₂ method are fulfilled in order to control the type I error rate.

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives

Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.

A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (PBPK): Evaluation with a semi-mechanistic pharmacokinetic model

BACKGROUND AND OBJECTIVE

The aims of this study are (i) to assess the predictive reliability of the physiologically based software PhysPK versus the well-known population approach software NONMEM for the cited semi-mechanistic PK model, (ii) to determine whether these modelling approaches are interchangeable and (iii) to compare acausal with causal modelling approaches in the framework of semi-mechanistic PK models.

METHODS

A semi-mechanistic model was proposed, which assumed oral administration of a solid dosage form with a peripheral compartment and two active metabolites. The model incorporates intestinal transit, dissolution limited by solubility, variable efflux transporter expression along the gut and linear and non-linear metabolism in the gut and liver. Four different approximations to the theoretical model were developed in order to validate both the new software and modelling methodology.

RESULTS

Plasmatic concentrations correlation plots as well as relative errors in AUC_0-48 and C_max predictions revealed the accuracy of PhysPK in the prediction of these exposition parameters. Physiological and acausal object oriented version systematically under-estimated AUC_0-48 and C_max of the parent drug, whereas metabolites were over-estimated when taking the semi-mechanistic and extraction-based metabolism version as the reference.

CONCLUSIONS

PhysPK has been properly validated, where differences are related to numerical precision of integrators and solvers. A systematic bias for the parent drug and active metabolites was predicted when a semi-mechanistic approach including extraction-based metabolism was compared to the physiologic and acausal approach, showing that interchangeability might be possible when intrinsic-clearance metabolism is implemented in the semi-mechanistic approach. The acausal and object-oriented methodology allows for defining the semi-mechanistic model through its local mechanisms and relationships among entities, without the need to build the final set of Ordinary Differential Equations.

IVIVC approach based on carbamazepine bioequivalence studies combination

The aim of the present study was to explore the feasibility of obtaining an IVIVC by combination of data from two bioequivalence (BE) studies of carbamazepine (CBZ) in order to assess if the previously published dissolution media and conditions could be applicable to any other oral immediate release (IR) CBZ products with conventional excipients. Twenty-four healthy male subjects from two BE study received one IR dose of the test (test 1 or 2) or the reference formulation (Tegretol, 400 mg). Dissolution studies of the IR CBZ tablets were performed in two different laboratories. In order to develop IVIVC, individual or average data analysis were considered. A level C, level B and level A correlation have been successfully developed by combining data from different BE studies of CBZ immediate release drug products. A level A IVIVC was developed with all four datasets with a good R2 for all the combinations of in vivo and in vitro data. A dissolution medium containing 1% SLS has demonstrated its suitability as the universal biopredictive dissolution medium, even if different batches and in vivo/in vitro studies were combined.

Semi-mechanistic Pharmacokinetic/Pharmacodynamic model of three pegylated rHuEPO and ior®EPOCIM in New Zealand rabbits

Marketed formulations of erythropoietin (EPO) ior®EPOCIM, MIRCERA® and two newly developed pegylated-EPO analogues (PEG-EPO 32 and 40 kDa) formulations were intravenously administered to New Zealand rabbits. A semi-mechanistic Pharmacokinetic/Pharmacodynamic (PK/PD) model describing in a simultaneous and integrated form the time course of reticulocytes, red blood cells and hemoglobin was built to account for the time course of hematopoiesis stimulation after erythropoietin administration. Data analysis was performed based on the population approach with the software NONMEM version 7.3. Erythropoietin disposition of each of the administered formulations was best described with a two compartment model and linear elimination. Different formulations show different clearance and apparent volume of distribution of the central compartment but share estimates of inter-compartmental clearance and apparent peripheral volume of distribution. A semi-mechanistic model including cell proliferation, maturation, and homeostatic regulation provided a good description of the data regardless the type of erythropoietin formulation administered. The system-, and drug-related parameters showed consistency and differed across formulations, respectively. A single IV administration of PEG-EPO 32 and 40 kDa formulations in New Zealand rabbits achieves a median change of 27% and 22% on RET levels, and of 47% and 63% on RBC and HGB levels, respectively compared to MIRCERA®. The administration of new branched PEG-chains formulations improves PK and PD properties of EPO, in terms of increasing elimination half-lives and pharmacological activity on RET, RBC and HGB compared to commercially available formulations (ior®EPOCIM and MIRCERA®).

Defining level A IVIVC dissolution specifications based on individual in vitro dissolution profiles of a controlled release formulation

Regulatory guidelines recommend that, when a level A IVIVC is established, dissolution specification should be established using averaged data and the maximum difference between AUC and C_max between the reference and test formulations cannot be greater than 20%. However, averaging data assumes a loss of information and may reflect a bias in the results. The objective of the current work is to present a new approach to establish dissolution specifications using a new methodology (individual approach) instead of average data (classical approach). Different scenarios were established based on the relationship between in vitro-in vivo dissolution rate coefficient using a level A IVIVC of a controlled release formulation. Then, in order to compare this new approach with the classical one, six additional batches were simulated. For each batch, 1000 simulations of a dissolution assay were run. C_max ratios between the reference formulation and each batch were calculated showing that the individual approach was more sensitive and able to detect differences between the reference and the batch formulation compared to the classical approach. Additionally, the new methodology displays wider dissolution specification limits than the classical approach, ensuring that any tablet from the new batch would generate in vivo profiles which its AUC or C_max ratio will be out of the 0.8-1.25 range, taking into account the in vitro and in vivo variability of the new batches developed.

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day^-1 No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.

Computer simulations for bioequivalence trials: Selection of analyte in BCS class II and IV drugs with first-pass metabolism, two metabolic pathways and intestinal efflux transporter

A semi-physiological two compartment pharmacokinetic model with two active metabolites (primary (PM) and secondary metabolites (SM)) with saturable and non-saturable pre-systemic efflux transporter, intestinal and hepatic metabolism has been developed. The aim of this work is to explore in several scenarios which analyte (parent drug or any of the metabolites) is the most sensitive to changes in drug product performance (i.e. differences in in vivo dissolution) and to make recommendations based on the simulations outcome. A total of 128 scenarios (2 Biopharmaceutics Classification System (BCS) drug types, 2 levels of K_{M Pgp}, in 4 metabolic scenarios at 2 dose levels in 4 quality levels of the drug product) were simulated for BCS class II and IV drugs. Monte Carlo simulations of all bioequivalence studies were performed in NONMEM 7.3. Results showed the parent drug (PD) was the most sensitive analyte for bioequivalence trials in all the studied scenarios. PM and SM revealed less or the same sensitivity to detect differences in pharmaceutical quality as the PD. Another relevant result is that mean point estimate of C_max and AUC methodology from Monte Carlo simulations allows to select more accurately the most sensitive analyte compared to the criterion on the percentage of failed or successful BE studies, even for metabolites which frequently show greater variability than PD.

Target-Site Investigation for the Plasma Prolactin Response: Mechanism-Based Pharmacokinetic-Pharmacodynamic Analysis of Risperidone and Paliperidone in the Rat

To understand the drivers in the biological system response to dopamine D2 receptor antagonists, a mechanistic semiphysiologically based (PB) pharmacokinetic-pharmacodymanic (PKPD) model was developed to describe prolactin responses to risperidone (RIS) and its active metabolite paliperidone (PAL). We performed a microdialysis study in rats to obtain detailed plasma, brain extracellular fluid (ECF), and cerebrospinal fluid (CSF) concentrations of PAL and RIS. To assess the impact of P-glycoprotein (P-gp) functioning on brain distribution, we performed experiments in the absence or presence of the P-gp inhibitor tariquidar (TQD). PK and PKPD modeling was performed by nonlinear mixed-effect modeling. Plasma, brain ECF, and CSF PK values of RIS and PAL were well described by a 12-compartmental semi-PBPK model, including metabolic conversion of RIS to PAL. P-gp efflux functionality was identified on brain ECF for RIS and PAL and on CSF only for PAL. In the PKPD analysis, the plasma drug concentrations were more relevant than brain ECF or CSF concentrations to explain the prolactin response; the estimated EC₅₀ was in accordance with reports in the literature for both RIS and PAL. We conclude that for RIS and PAL, the plasma concentrations better explain the prolactin response than do brain ECF or CSF concentrations. This research shows that PKPD modeling is of high value to delineate the target site of drugs.

Investigating the Discriminatory Power of BCS-Biowaiver in Vitro Methodology to Detect Bioavailability Differences between Immediate Release Products Containing a Class I Drug

The purpose of this work is to investigate the discriminatory power of the Biopharmaceutics Classification System (BCS)-biowaiver in vitro methodology, i.e., to investigate if a BCS-biowaiver approach would have detected the Cmax differences observed between two zolpidem tablets and to identify the cause of the in vivo difference. Several dissolution conditions were tested with three zolpidem formulations: the reference (Stilnox), a bioequivalent formulation (BE), and a nonbioequivalent formulation (N-BE). Zolpidem is highly soluble at pH 1.2, 4.5, and 6.8. Its permeability in Caco-2 cells is higher than that of metoprolol and its transport mechanism is passive diffusion. None of the excipients (alone or in combination) showed any effect on permeability. All formulations dissolved more than 85% in 15 min in the paddle apparatus at 50 rpm in all dissolution media. However, at 30 rpm the nonbioequivalent formulation exhibited a slower dissolution rate. A slower gastric emptying rate was also observed in rats for the nonbioequivalent formulation. A slower disintegration and dissolution or a delay in gastric emptying might explain the Cmax infra-bioavailability for a highly permeable drug with short half-life. The BCS-biowaiver approach would have declared bioequivalence, although the in vivo study was not conclusive but detected a 14% mean difference in Cmax that precluded the bioequivalence demonstration. Nonetheless, these findings suggest that a slower dissolution rate is more discriminatory and that rotation speeds higher than 50 rpm should not be used in BCS-biowaivers, even if a coning effect occurs.

Validation of a semi-physiological model for caffeine in healthy subjects and cirrhotic patients

The objective of this paper was to validate a previously developed semi physiological model to simulate bioequivalence trials of drug products. The aim of the model was to ascertain whether the measurement of the metabolite concentration-time profiles would provide any additional information in bioequivalence studies (Fernandez-Teruel et al., 2009a,b; Navarro-Fontestad et al., 2010). The semi-physiological model implemented in NONMEM VI was used to simulate caffeine and its main metabolite plasma levels using caffeine parameters from bibliography. Data from 3 bioequivalence studies in healthy subjects at 3 different doses (100, 175 and 400mg of caffeine) and one study in cirrhotic patients (200 or 250mg) were used. The first aim was to adapt the previous semi-physiological model for caffeine, showing the hepatic metabolism with one main metabolite, paraxanthine. The second aim was to validate the model by comparison of the simulated plasma levels of parent drug and metabolite to the experimental data. The simulations have shown that the proposed semi-physiological model was able to reproduce adequately the pharmacokinetic behavior of caffeine and paraxanthine in both healthy subjects and cirrhotic patients at all the assayed doses. Therefore, the model could be used to simulate plasma concentrations vs. time of drugs with the same pharmacokinetic scheme as caffeine, as long as their population parameters are known, and it could be useful for bioequivalence trial simulation of drugs that undergo hepatic metabolism with a single main metabolite.

Tubulin acetylation promoting potency and absorption efficacy of deacetylase inhibitors

BACKGROUND AND PURPOSE

Histone deacetylase 6 (HDAC6) and silent information regulator 2 (SIRT2) control the dynamics of the microtubule network via their deacetylase activities. Tubulin polymerization promoting protein (TPPP/p25) enhances microtubule acetylation by its direct binding to HDAC6. Our objective was to characterize the multiple interactions of the deacetylases and to establish the inhibitory potency and the pharmacokinetic features of the deacetylase inhibitors, trichostatin A (TSA) and AGK2.

EXPERIMENTAL APPROACH

The interactions of deacetylases with tubulin and TPPP/p25 were quantified by elisa using human recombinant proteins. The effect of inhibitors on the tubulin acetylation was established in HeLa cells transfected with pTPPP and CG-4 cells expressing TPPP/p25 endogenously by celisa (elisa on cells), Western blot and immunofluorescence microscopy. The pharmacokinetic features of the inhibitors were evaluated by in situ kinetic modelling of their intestinal transport in rats.

KEY RESULTS

Deacetylases interact with both tubulin and TPPP/p25, notwithstanding piggy-back binding of HDAC6 or SIRT2 to the TPPP/p25-associated tubulin was established. Much higher inhibitory potency for TSA than for AGK2 was detected in both HeLa and CG-4 cells. Pioneer pharmacokinetic studies revealed passive diffusion and diffusion coupled with secretion for TSA and AGK2 respectively. Both inhibitors exhibited greater permeability than some other well-established drugs.

CONCLUSIONS AND IMPLICATIONS

TPPP/p25-directed deacetylase inhibition provides mechanisms for the fine control of the dynamics and stability of the microtubule network. Deacetylase inhibitors with chemical structures similar to TSA and AGK2 appear to be excellent candidates for oral drug absorption.

Innovative in vitro method to predict rate and extent of drug delivery to the brain across the blood-brain barrier

The relevant parameters for predicting rate and extent of access across the blood-brain barrier (BBB) are fu,plasma (unbound fraction in plasma), Vu,brain (distribution volume in brain) and Kp,uu,brain (ratio of free concentrations in plasma and brain). Their estimation still requires animal studies and in vitro low throughput experiments which make difficult the screening of new CNS candidates. The aim of the present work was to develop a new whole in vitro high throughput method to predict drug rate and extent of access across the BBB. The system permits estimation of fu,plasma, Vu,brain and Kp,uu,brain in a single experimental system, using in vitro cell monolayers in different conditions. From the ratios of the apparent permeability values (Papp) with the adequate mathematical analysis the relevant parameters can be estimated. Papp of ten model compounds has been obtained in MDCKII and MDCK-Mdr1cell monolayers in the absence and presence of albumin and brain homogenate. The ratio of Papp in the absence and presence of albumin allows estimation of in vitro fu,plasma. Papp in the presence of brain homogenate is used to estimate fu,brain and Vu,brain. Kp,uu,brain is estimated from the apical to basal versus basal to apical clearances. The BBB parameters obtained with the new method were predictive of the in vivo behavior of candidates. In vitro fu,plasma, Kp,uu,brain and Vu,brain (calculated with Papp from MDCKII cell line) presented a good correlation with in vivo fu,plasma, Kp,uu,CSF and Vu,brain published values (r=0.92; r=0.85; and r=0.99 respectively). Despite its simplicity the predictive performance is fairly good considering the reduced number of tested compounds with different physicochemical and transport properties. Further experimental modifications could be checked to optimize the method, but the present data support its feasibility. As other in vitro cell culture models, the system is suitable for miniaturization and robotization to allow high throughput screening of CNS candidates.