Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: application of a double gamma absorption model with SAEM algorithm.

INTRODUCTION: Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients. MATERIAL AND METHODS: A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set. RESULTS: Mean patient age and dose was 13 years old (5-18) and 18.1 mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30 min, 2 h, and 3 h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%). CONCLUSION: The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population.

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity.

BACKGROUND AND OBJECTIVE: In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods. METHODS: Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels. RESULTS: Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values. CONCLUSIONS: This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting.

BACKGROUND: Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization. METHODS: Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations. RESULTS: The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance. CONCLUSIONS: Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.

Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data.

The development of in silico tools able to predict bioactivity and toxicity of chemical substances is a powerful solution envisioned to assess toxicity as early as possible. To enable the development of such tools, the ToxCast program has generated and made publicly available in vitro bioactivity data for thousands of compounds. The goal of the present study is to characterize and explore the data from ToxCast in terms of Machine Learning capability. For this, a large scale analysis on the entire database has been performed to build models to predict bioactivities measured in in vitro assays. Simple classical QSAR algorithms (ANN, SVM, LDA, random forest, and Bayesian) were first applied on the data, and the results of these algorithms suggested that they do not seem to be well-suited for data sets with a high proportion of inactive compounds. The study then showed for the first time that the use of an ensemble method named "Stacked generalization" could improve the model performance on this type of data. Indeed, for 61% of 483 models, the Stacked method led to models with higher performance. Moreover, the combination of this ensemble method with an applicability domain filter allows one to assess the reliability of the predictions for further compound prioritization. In particular we showed that for 50% of the models, the ROC score is better if we do not consider the compounds that are not within the applicability domain.

The HIF-1-inducible lysyl oxidase activates HIF-1 via the Akt pathway in a positive regulation loop and synergizes with HIF-1 in promoting tumor cell growth.

Adaptation to hypoxia is a driving force for tumor progression that leads to therapy resistance and poor clinical outcome. Hypoxic responses are mainly mediated by hypoxia-inducible transcription factor-1 (HIF-1). One critical HIF-1 target mediating tumor progression is lysyl oxidase (LOX), which catalyzes cross-linking of collagens and elastin in the extracellular matrix, thereby regulating tissue tensile strength. Paradoxically, LOX has been reported to be both upregulated and downregulated in cancer cells, especially in colorectal cancer. Thus, we hypothesized that LOX might regulate expression of HIF-1 to create a self-timing regulatory circuit. Using human colorectal carcinoma cell lines in which HIF-1 and LOX expression could be modulated, we showed that LOX induction enhanced HIF-1 expression, whereas LOX silencing reduced it. Mechanistic investigations revealed that LOX activated the PI3K (phosphoinositide 3-kinase)-Akt signaling pathway, thereby upregulating HIF-1α protein synthesis in a manner requiring LOX-mediated hydrogen peroxide production. Consistent with these results, cancer cell proliferation was stimulated by secreted and active LOX in an HIF-1α-dependent fashion. Furthermore, nude mice xenograft assays established that HIF-1 potentiated LOX action on tumor growth in vivo. Taken together, these findings provide compelling evidence that LOX and HIF-1 act in synergy to foster tumor formation, and they suggest that HIF-1/LOX mutual regulation is a pivotal mechanism in the adaptation of tumor cells to hypoxia.

Gene regulation in response to graded hypoxia: the non-redundant roles of the oxygen sensors PHD and FIH in the HIF pathway.

UNLABELLED: Hypoxia Inducible Factor (HIF), being the master protein involved in adaptation to low pO(2), plays a major role in many physiological and pathological phenomena: development, inflammation, ischemia and cancer. Prolyl hydroxylase (PHD) and factor inhibiting HIF (FIH) are the two oxygen sensors that regulate the HIF pathway. Here we model the regulatory dynamics in an oxygen gradient by a system of differential equations. A part of the work consists in a qualitative analysis, driven independently of the values of the parameters, which explains the non-redundant functional roles of FIH and PHD. In a second part, we use biological experiments to fit the model in a physiologically relevant context and run simulations. Simulation results are confronted with success to independent biological experiments. The combination of biological data and mathematical analysis stresses that FIH is a fine modulator determining whether a given gene should be induced in mildly or in strongly hypoxic areas. Moreover it gives access to other functional predictions that are not directly accessible by pure experiments, for instance the stoichiometry of prolyl-hydroxylation on HIF, and the switch-like properties of the system. AVAILABILITY: an interactive simulation interface is available at http://sdi.ljad.free.fr/spip.php?article111.

Hypoxia-inducible carbonic anhydrase IX and XII promote tumor cell growth by counteracting acidosis through the regulation of the intracellular pH.

Acidosis of the tumor microenvironment is typical of a malignant phenotype, particularly in hypoxic tumors. All cells express multiple isoforms of carbonic anhydrase (CA), enzymes catalyzing the reversible hydration of carbon dioxide into bicarbonate and protons. Tumor cells express membrane-bound CAIX and CAXII that are controlled via the hypoxia-inducible factor (HIF). Despite the recognition that tumor expression of HIF-1alpha and CAIX correlates with poor patient survival, the role of CAIX and CAXII in tumor growth is not fully resolved. To understand the advantage that tumor cells derive from expression of both CAIX and CAXII, we set up experiments to either force or invalidate the expression of these enzymes. In hypoxic LS174Tr tumor cells expressing either one or both CA isoforms, we show that (a) in response to a "CO(2) load," both CAs contribute to extracellular acidification and (b) both contribute to maintain a more alkaline resting intracellular pH (pH(i)), an action that preserves ATP levels and cell survival in a range of acidic outside pH (6.0-6.8) and low bicarbonate medium. In vivo experiments show that ca9 silencing alone leads to a 40% reduction in xenograft tumor volume with up-regulation of ca12 mRNA levels, whereas invalidation of both CAIX and CAXII gives an impressive 85% reduction. Thus, hypoxia-induced CAIX and CAXII are major tumor prosurvival pH(i)-regulating enzymes, and their combined targeting shows that they hold potential as anticancer targets.

Activation of HIF-1alpha in exponentially growing cells via hypoxic stimulation is independent of the Akt/mTOR pathway.

Accumulation of HIF-1alpha during normoxic conditions at high cell density has previously been shown to occur and can be used to stabilize HIF-1alpha protein in the absence of a specific anaerobic chamber. However, the impact and origin of this pool of HIF-1alpha, obtained under normoxia, has been underestimated. In this study, we have systematically compared the related pools of HIF-1alpha stabilized in normoxia by high cell density to those obtained at low density in hypoxia. At first glance, these two stimuli appear to have similar outcomes: HIF-1alpha stabilization and induction of HIF-1-dependent genes. However, upon careful analysis, we observed that molecular mechanisms involved are different. We clearly demonstrate that density-dependant HIF-1alpha accumulation during normoxia is due to the cells high consumption of oxygen, as demonstrated by using a respiration inhibitor (oligomycin) and respiratory-defective mutant cells (GSK3). Finally and most importantly, our data indicate that a decrease in AKT activity followed by a total decrease in p70(S6K) phosphorylation reflecting a decrease in mTOR activity occurs during high oxygen consumption, resulting from high cell density. In contrast, hypoxia, even at severe low O(2) levels, only slightly impacts upon the mTOR pathway under low cell density conditions. Thus, activation of HIF-1alpha in exponentially growing cells via hypoxic stimulation is independent of the Akt/mTOR pathway whereas HIF-1alpha activation obtained in high confluency is totally dependent on mTOR pathway as rapamycin totally impaired (i) HIF-1alpha stabilization and (ii) mRNA levels of CA9 and BNIP3, two HIF-target genes.

A dialogue between the hypoxia-inducible factor and the tumor microenvironment.

The hypoxia-inducible factor is the key protein responsible for the cellular adaptation to low oxygen tension. This transcription factor becomes activated as a result of a drop in the partial pressure of oxygen, to hypoxic levels below 5% oxygen, and targets a panel of genes involved in maintenance of oxygen homeostasis. Hypoxia is a common characteristic of the microenvironment of solid tumors and, through activation of the hypoxia-inducible factor, is at the center of the growth dynamics of tumor cells. Not only does the microenvironment impact on the hypoxia-inducible factor but this factor impacts on microenvironmental features, such as pH, nutrient availability, metabolism and the extracellular matrix. In this review we discuss the influence the tumor environment has on the hypoxia-inducible factor and outline the role of this factor as a modulator of the microenvironment and as a powerful actor in tumor remodeling. From a fundamental research point of view the hypoxia-inducible factor is at the center of a signaling pathway that must be deciphered to fully understand the dynamics of the tumor microenvironment. From a translational and pharmacological research point of view the hypoxia-inducible factor and its induced downstream gene products may provide information on patient prognosis and offer promising targets that open perspectives for novel "anti-microenvironment" directed therapies.

Hypoxia signalling in cancer and approaches to enforce tumour regression.

Tumour cells emerge as a result of genetic alteration of signal circuitries promoting cell growth and survival, whereas their expansion relies on nutrient supply. Oxygen limitation is central in controlling neovascularization, glucose metabolism, survival and tumour spread. This pleiotropic action is orchestrated by hypoxia-inducible factor (HIF), which is a master transcriptional factor in nutrient stress signalling. Understanding the role of HIF in intracellular pH (pH(i)) regulation, metabolism, cell invasion, autophagy and cell death is crucial for developing novel anticancer therapies. There are new approaches to enforce necrotic cell death and tumour regression by targeting tumour metabolism and pH(i)-control systems.

The oxygen sensor factor-inhibiting hypoxia-inducible factor-1 controls expression of distinct genes through the bifunctional transcriptional character of hypoxia-inducible factor-1alpha.

The function of the hypoxia-inducible factor-1 (HIF-1), the key transcription factor involved in cellular adaptation to hypoxia, is restricted to low oxygen tension (pO(2)). As such, this transcription factor is central in modulating the tumor microenvironment, sensing nutrient availability, and controlling anaerobic glycolysis, intracellular pH, and cell survival. Degradation and inhibition of the limiting HIF-1alpha subunit are intimately connected in normoxia. Hydroxylation of two proline residues by prolyl hydroxylase domain (PHD) 2 protein earmarks the protein for degradation, whereas hydroxylation of an asparagine residue by factor-inhibiting HIF-1 (FIH-1 or FIH) reduces its transcriptional activity. Indeed, silencing of either PHD2 or FIH in normoxia partially induced hypoxic genes, whereas combined PHD2/FIH silencing generated a full hypoxic gene response. Given the fact that HIF-1alpha possesses two transcriptional activation domains [TAD; NH(2)-terminal (N-TAD) and COOH-terminal (C-TAD)], we hypothesized on a possible bifunctional activity of HIF-1alpha that could be discriminated by FIH, an inhibitor of the C-TAD. In human cell lines engineered to overexpress or silence FIH in response to tetracycline, we show by quantitative reverse transcription-PCR that a set of hypoxic genes (ca9, phd3, pgk1, and bnip3) respond differently toward FIH expression. This finding, extended to 26 hypoxia-induced genes, indicates differential gene expression by the N-TAD and C-TAD in response to the hypoxic gradient. We propose that the oxygen-sensitive attenuator FIH, together with two distinct TADs, is central in setting the gene expression repertoire dictated by the cell pO(2).

HIF-1: master and commander of the hypoxic world. A pharmacological approach to its regulation by siRNAs.

The hypoxia-inducible factor-1 (HIF-1) is primarily involved in the sensing and adapting of cells to changes in the O2 level, which is essential for their viability. It is important that this critical transcription factor be tightly regulated in order for cells to respond to a wide range of O2 concentrations. HIF-1 regulation by post-translational modification is the central theme of the scenario of O2 homeostasis. The alpha subunit of HIF-1 is the principal actor while the supporting actors (PHDs, FIH-1, ARD1, CITED2, p300...) all contribute to the complexity of the grand finale. It is well established that HIF-1 expression and activation correlates with tumor progression and resistance to cancer treatments. We will introduce the different actors involved in HIF-1 regulation, and their mechanisms of action via invalidation by siRNAs and discuss therapies targeting HIF-1, to selectively kill tumor cells that adapt to low O2 concentrations.