Sihem Ait-Oudhia

Application of Pharmacokinetic and Pharmacodynamic Analysis to the Development of Liposomal Formulations for Oncology

Liposomal formulations of anticancer agents have been developed to prolong drug circulating lifetime, enhance anti-tumor efficacy by increasing tumor drug deposition, and reduce drug toxicity by avoiding critical normal tissues. Despite the clinical approval of numerous liposome-based chemotherapeutics, challenges remain in the development and clinical deployment of micro- and nano-particulate formulations, as well as combining these novel agents with conventional drugs and standard-of-care therapies. Factors requiring optimization include control of drug biodistribution, release rates of the encapsulated drug, and uptake by target cells. Quantitative mathematical modeling of formulation performance can provide an important tool for understanding drug transport, uptake, and disposition processes, as well as their role in therapeutic outcomes. This review identifies several relevant pharmacokinetic/pharmacodynamic models that incorporate key physical, biochemical, and physiological processes involved in delivery of oncology drugs by liposomal formulations. They capture observed data, lend insight into factors determining overall antitumor response, and in some cases, predict conditions for optimizing chemotherapy combinations that include nanoparticulate drug carriers.

Expansion of the neonatal platelet mass is achieved via an extension of platelet lifespan.

The fetal/neonatal hematopoietic system must generate enough blood cells to meet the demands of rapid growth. This unique challenge might underlie the high incidence of thrombocytopenia among preterm neonates. In this study, neonatal platelet production and turnover were investigated in newborn mice. Based on a combination of blood volume expansion and increasing platelet counts, the platelet mass increased sevenfold during the first 2 weeks of murine life, a time during which thrombopoiesis shifted from liver to bone marrow. Studies applying in vivo biotinylation and mathematical modeling showed that newborn and adult mice had similar platelet production rates, but neonatal platelets survived 1 day longer in circulation. This prolonged lifespan fully accounted for the rise in platelet counts observed during the second week of murine postnatal life. A study of pro-apoptotic and anti-apoptotic Bcl-2 family proteins showed that neonatal platelets had higher levels of the anti-apoptotic protein Bcl-2 and were more resistant to apoptosis induced by the Bcl-2/Bcl-xL inhibitor ABT-737 than adult platelets. However, genetic ablation or pharmacologic inhibition of Bcl-2 alone did not shorten neonatal platelet survival or reduce platelet counts in newborn mice, indicating the existence of redundant or alternative mechanisms mediating the prolonged lifespan of neonatal platelets.

Meta-analysis of Nanoparticulate Paclitaxel Delivery System Pharmacokinetics and Model Prediction of Associated Neutropenia

PurposeNanoparticulate paclitaxel carriers have entered clinical evaluation as alternatives to the Cremophor-based standard Taxol® (Cre-pac). Their pharmacokinetics (PK) is complex, and factors influencing their pharmacodynamics (PD) are poorly understood. We aimed to develop a unified quantitative model for 4 paclitaxel carriers that captures systems-level PK, predicts micro-scale PK processes, and permits correlations between carrier properties and observed PD.MethodsData consisting of 54 PK profiles and 574 observations were extracted from 20 clinical studies investigating Cre-pac, albumin-(A-pac), liposome-(L-pac), and tocopherol-(T-pac) nanocarriers. A population-PK approach was used for data analysis. All datasets were simultaneously fitted to produce a unified model. Model-based simulations explored relationships between predicted PK and myelosuppression for each formulation.ResultsThe final model employed nonlinear drug-binding mechanisms to describe Cre-pac and a delayed-release model for A-pac, L-pac, and T-pac. Estimated drug-release rate constants (h−1): Cre-pac (5.19), L-pac (1.26), A-pac (0.72), T-pac (0.74). Simulations of equivalent dosing schemes ranked neutropenia severity (highest to lowest): T-pac~Cre-pac>L-pac~A-pac and predicted remarkably well the clinically-observed relationships between neutropenia and free drug exposure relative to a threshold concentration.ConclusionsPaclitaxel disposition was well-described for all formulations. The derived model predicts toxicodynamics of diverse paclitaxel carriers.

Systems Pharmacological Analysis of Paclitaxel-Mediated Tumor Priming That Enhances Nanocarrier Deposition and Efficacy

Paclitaxel (PAC)-mediated apoptosis decompresses and primes tumors for enhanced deposition of nanoparticulate agents such as pegylated liposomal doxorubicin (DXR). A quantitative pharmacokinetic/pharmacodynamic (PK/PD) approach was developed to analyze efficacy and identify optima for PAC combined with sterically stabilized liposome (SSL)-DXR. Using data extracted from diverse literature sources, Cremophor-paclitaxel (Taxol®) PK was described by a carrier-mediated dispositional model and SSL-DXR PK was described by a two-compartment model with first-order drug release. A hybrid-physiologic, well-stirred model with partition coefficients (Kp) captured intratumor concentrations. Apoptotic responses driving tumor priming were modeled using nonlinear, time-dependent transduction functions. The tumor growth model used net first-order growth and death rate constants, and two transit compartments that captured the temporal displacement of tumor exposure versus effect, and apoptotic signals from each agent were used to drive cytotoxic effects of the combination. The final model captured plasma and intratumor PK data, apoptosis induction profiles, and tumor growth for all treatments/sequences. A feedback loop representing PAC-induced apoptosis effects on Kp_DXR enabled the model to capture tumor-priming effects. Simulations to explore time- and sequence-dependent effects of priming indicated that PAC priming increased Kp_DXR 3-fold. The intratumor concentrations producing maximal and half-maximal effects were 18 and 7.2 μg/ml for PAC, and 17.6 and 14.3 μg/ml for SSL-DXR. The duration of drug-induced apoptosis was 27.4 h for PAC and 15.8 h for SSL-DXR. Simulations suggested that PAC administered 24 h before peak priming could increase efficacy 2.5-fold over experimentally reported results. The quantitative approach developed in this article is applicable for evaluating tumor-priming strategies using diverse agents.

Advancing Cancer Therapy with Present and Emerging Immuno-Oncology Approaches

Immuno-oncology (I-O) is a young and growing field on the frontier of cancer therapy. Contrary to cancer therapies that directly target malignant cells, I-O therapies stimulate the body’s immune system to target and attack the tumor, which is otherwise invisible to, or inhibiting the immune response. To this end, several methods have been developed: First, passive therapies that enable T-cells to fight the tumor without direct manipulation, typically through binding and modifying the intracellular signaling of surface receptors. Checkpoint inhibitors, perhaps the most well known of I-O therapies; are an example of such. These are monoclonal antibodies that block binding of the tumor cell at receptors that inactivate the T-cell. A variety of small molecules can achieve the same effect by affecting metabolic or signaling pathways to boost the immune response or prevent its attenuation. Drugs originally formulated for unrelated disease states are now being used to treat cancer under the I-O approach. Second, active therapies which often involve direct manipulations that occur in vitro and once introduced to the patient will directly attack the tumor. Adoptive cell transfer is the oldest of these methods. It involves the removal of T-cells from the body, which are then expanded and genetically modified for specificity toward tumor-associated antigens (TAAs), and then reintroduced to the patient. A similar approach is taken with cancer vaccines, where TAAs are identified and reintroduced with adjuvants to stimulate an immune response, sometimes in the context of antigen-presenting cells or viral vectors. Oncolytic viruses are genetically modified natural viruses for selectivity toward tumor cells. The resulting cytotoxicity has the potential to elicit an immune response that furthers tumor cell killing. A final active approach is bi-specific T-cell engagers. These modified antibodies act to link a T-cell and tumor cell through surface receptors and thereby forcibly generate immune recognition. The therapies in each of these subfields are all still very new and ongoing clinical trials could provide even further additions. The full therapeutic potential of the aforementioned therapies, alone or in combination, has yet to be realized, but holds great promise for the future of cancer treatment.

Simultaneous Pharmacokinetics/Pharmacodynamics Modeling of Recombinant Human Erythropoietin upon Multiple Intravenous Dosing in Rats

A pharmacokinetics (PK)/pharmacodynamics (PD) model was developed to describe the tolerance and rebound for reticulocyte (RET) and red blood cell (RBC) counts and the hemoglobin (Hb) concentrations in blood after repeated intravenous administrations of 1350 IU/kg of recombinant human erythropoietin (rHuEPO) in rats thrice weekly for 6 weeks. Drug concentrations were described by using a quasi-equilibrium model. The PD model consisted of a lifespan-based indirect response model (LIDR) with progenitor cells [burst colony-forming unit erythroblasts and colony-forming unit erythroblasts (CFUs)], normoblasts (NOR), RETs, and RBCs. Drug–receptor complex stimulatory effects on progenitor cells differentiation and RBC lifespan were expressed by using the Emax model (Smax-epo and SC50-epo, Emax and EC50). The Hb profile was indirectly modeled through a LIDR model for mean corpuscular hemoglobin (with a lifespan Tmch) including a linear (Smax-mch) drug stimulatory effect. The negative feedback from RBCs accounted for the time-dependent rHuEPO clearance decline. A simultaneous PK/PD fitting was performed by using MATLAB-based software. PK parameters such as equilibrium dissociation, erythropoietin receptor degradation, production, and internalization rate constants were 0.18 nM (fixed), 0.08 h−1, 0.03 nM/h, and 2.51 h−1, respectively. The elimination rate constant and central volume of distribution were 0.57 h−1 and 40.63 ml/kg, respectively. CFU and NOR, RET, and RBC lifespans were 37.26 h, 17.25 h, and 30.15 days, respectively. Smax-epo and SC50-epo were 7.3 and 0.47 10−2 nM, respectively. Emax was fixed to 1. EC50 and SC50-epo were equal. Smax-mch and Tmch were 168.1 nM−1 and 35.15 days, respectively. The proposed PK/PD model effectively described rHuEPO nonstationary PK and allowed physiological estimates of cell lifespans.

Flow cytometric analysis of reticulocyte maturation after erythropoietin administration in rats

We have proposed a technique for transforming reticulocyte (RET) flow cytometry count into the age distribution allowing for a quantitative characterization of RETs dynamics. This technique was evaluated in homeostatic and erythropoietically stimulated rats. We administered a single intravenous dose (4,050 IU/kg) of recombinant human erythropoietin to Wistar rats, then stained their RETs with thiazole orange, and measured the distribution of the fluorescent signal using flow cytometry. The RET age distribution was determined assuming an exponential decline of RET RNA. The RET distribution over time was characterized by median age, production rate of mature red blood cell in the blood, bone marrow and blood maturation times, and RET loss from the blood. Erythropoietin was found to cause complex changes not only in RET count but also in age distribution. Generally, an increase in RET count was accompanied by the presence of young RETs and a decrease with the presence of old RETs. The rate at which RETs were removed from the blood was affected considerably by erythropoietin administration, and their death was attributed to it. Flow cytometric analysis of RET age distribution expands our understanding of erythropoiesis and augments the information we can obtain from RET measurements.

Current advances in biomarkers for targeted therapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2, estrogen receptor, and progesterone receptor. Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African-Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically accepted targets for tailored therapy, warranting the need for candidate biomarkers. BiomarkerBase, an online platform used to find biomarkers reported in clinical trials, was utilized to screen all potential biomarkers for TNBC and select only the ones registered in completed TNBC trials through clinicaltrials.gov. The selected candidate biomarkers were classified as surrogate, prognostic, predictive, or pharmacodynamic (PD) and organized by location in the blood, on the cell surface, in the cytoplasm, or in the nucleus. Blood biomarkers include vascular endothelial growth factor/vascular endothelial growth factor receptor and interleukin-8 (IL-8); cell surface biomarkers include EGFR, insulin-like growth factor binding protein, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA, pAKT/S6/p4E-BP1, PTEN, ALDH1, and the PIK3CA/AKT/mTOR-related metabolites; and nucleus biomarkers include BRCA1, the gluco-corticoid receptor, TP53, and Ki67. Candidate biomarkers were further organized into a “cellular protein network” that demonstrates potential connectivity. This review provides an inventory and reference point for promising biomarkers for breakthrough targeted therapies in TNBC.

Pharmacodynamic Modeling of Cell Cycle Effects for Gemcitabine and Trabectedin Combinations in Pancreatic Cancer Cells

Combinations of gemcitabine and trabectedin exert modest synergistic cytotoxic effects on two pancreatic cancer cell lines. Here, systems pharmacodynamic (PD) models that integrate cellular response data and extend a prototype model framework were developed to characterize dynamic changes in cell cycle phases of cancer cell subpopulations in response to gemcitabine and trabectedin as single agents and in combination. Extensive experimental data were obtained for two pancreatic cancer cell lines (MiaPaCa-2 and BxPC-3), including cell proliferation rates over 0–120 h of drug exposure, and the fraction of cells in different cell cycle phases or apoptosis. Cell cycle analysis demonstrated that gemcitabine induced cell cycle arrest in S phase, and trabectedin induced transient cell cycle arrest in S phase that progressed to G2/M phase. Over time, cells in the control group accumulated in G0/G1 phase. Systems cell cycle models were developed based on observed mechanisms and were used to characterize both cell proliferation and cell numbers in the sub G1, G0/G1, S, and G2/M phases in the control and drug-treated groups. The proposed mathematical models captured well both single and joint effects of gemcitabine and trabectedin. Interaction parameters were applied to quantify unexplainable drug-drug interaction effects on cell cycle arrest in S phase and in inducing apoptosis. The developed models were able to identify and quantify the different underlying interactions between gemcitabine and trabectedin, and captured well our large datasets in the dimensions of time, drug concentrations, and cellular subpopulations.

Pharmacokinetic and pharmacodynamic analysis comparing diverse effects of detomidine, medetomidine, and dexmedetomidine in the horse: a population analysis

The present study characterizes the pharmacokinetic (PK) and pharmacodynamic (PD) relationships of the α2-adrenergic receptor agonists detomidine (DET), medetomidine (MED) and dexmedetomidine (DEX) in parallel groups of horses from in vivo data after single bolus doses. Head height (HH), heart rate (HR), and blood glucose concentrations were measured over 6 h. Compartmental PK and minimal physiologically based PK (mPBPK) models were applied and incorporated into basic and extended indirect response models (IRM). Population PK/PD analysis was conducted using the Monolix software implementing the stochastic approximation expectation maximization algorithm. Marked reductions in HH and HR were found. The drug concentrations required to obtain inhibition at half-maximal effect (IC50 ) were approximately four times larger for DET than MED and DEX for both HH and HR. These effects were not gender dependent. Medetomidine had a greater influence on the increase in glucose concentration than DEX. The developed models demonstrate the use of mechanistic and mPBPK/PD models for the analysis of clinically obtainable in vivo data.