Immunosuppressant adherence in adult outpatient hematopoietic cell transplant recipients.

INTRODUCTION: Medication nonadherence continues to be challenging for allogeneic hematopoietic cell transplant (HCT) recipients. The risk and severity of chronic graft-versus-host disease (GVHD) are associated with low immunosuppressant concentrations (which can be improved with model-informed precision dosing (MIPD)) and with immunosuppressant nonadherence (which can be improved with acceptable interventions). METHODS: With the goals of improving adherence and achieving therapeutic concentrations of immunosuppressants to eliminate GVHD, we characterized the feasibility of using the Medication Event Monitoring (MEMS®) Cap in adult HCT recipients. RESULTS: Of the 27 participants offered the MEMS® Cap at the time of hospital discharge, 7 (25.9%) used it, which is below our a priori threshold of 70%. These data suggest the MEMS® Cap is not feasible for HCT recipients. The MEMS® Cap data were available for a median of 35 days per participant per medication (range: 7-109 days). The average daily adherence per participant ranged from 0 to 100%; four participants had an average daily adherence of over 80%. CONCLUSIONS: MIPD may be supported by MEMS® technology to provide the precise time of immunosuppressant self-administration. The MEMS® Cap was used by only a small percentage (25.9%) of HCT recipients in this pilot study. In accordance with larger studies using less accurate tools to evaluate adherence, immunosuppressant adherence varied from 0% to 100%. Future studies should establish the feasibility and clinical benefit of combining MIPD with newer technology, specifically the MEMS® Button, which can inform the oncology pharmacist of the time of immunosuppressant self-administration.

Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma.

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (kg= 0.130 vs. 0.0551 week-1, p-value < 0.0001), but similar for both groups (kg = 0.0624 vs.0.0563 week-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

Accelerating robust plausible virtual patient cohort generation by substituting ODE simulations with parameter space mapping.

The generation of plausible virtual patients (VPs) is an important step in most quantitative systems pharmacology (QSP) workflows, which requires time-intensive solving of ordinary differential equations (ODEs). However, non-physiological profiles of outputs of interest (OoI) are frequently produced, and additional acceptance/rejection steps are needed for comparing and removing VPs with predicted values outside a pre-defined range. Here, a new approach is developed to accelerate the acceptance/rejection steps by leveraging patterns of parameter associations with OoI. In most models, some parameters are monotonic with respect to OoI, such that an increase in a parameter value always induces an increase or decrease in the OoI. This monotonic property can be used to replace some ODE-solving steps with appropriate monotonic parameter value comparisons to extrapolate the rejection or interpolate the acceptance of some VPs (after simulation) to others. Two algorithms were built that directly extract plausible VPs from a pre-defined initial cohort. These algorithms were first tested using a simple tumor growth inhibition model. Analyzing 200,000 VPs took 50 s with a reference method and 3 to 41 s (depending on the initial set-up) with the first algorithm. The method was then applied to an apoptosis QSP model, in which the clinical phenotypes (i.e., treatment sensitive or resistant) of 200,000 VPs were fully characterized for four different drug regimens in 12 min as compared to over 80 min with the reference approach. Extraction of each phenotype can also be performed individually in 34 s to 8 min, demonstrating the time benefit and flexibility of this approach.

Synergistic Pharmacodynamic Effects of Gemcitabine and Fibroblast Growth Factor Receptor Inhibitors on Pancreatic Cancer Cell Cycle Kinetics and Proliferation.

Median survival of pancreatic ductal adenocarcinoma cancer (PDAC) is 6 months, with 9% 5-year survival. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and combination therapies integrating novel targeted agents could improve outcomes. Fibroblast growth factor (FGF) receptors (FGFRs) play important roles in PDAC growth and invasion. Therefore, FGFR inhibitors (FGFRi) merit further investigation. Efficacy of Gem combined with NVP-BGJ398, a pan-FGFRi, was investigated in multiple PDAC cell lines exposed to the drugs alone and combined. Cell cycle distribution and cell numbers were quantified over time. Two pharmacodynamic models were developed to investigate Gem/BGJ398 interactions quantitatively: a drug-mediated cell proliferation/death model, and a drug-perturbed cell cycle progression model. The models captured temporal changes in cell numbers, cell cycle progression, and cell death during drug exposure. Simultaneous fitting of all data provided reasonable parameter estimates. Therapeutic efficacy was then evaluated in a PDAC mouse model. Compared with Gem alone, combined Gem + FGFRi significantly downregulated ribonucleotide-diphosphate reductase large subunit 1 (RRM1), a gemcitabine resistance (GemR) biomarker, suggesting the FGFRi inhibited GemR emergence. The cell proliferation/death pharmacodynamic model estimated the drug interaction coefficient ψ death = 0.798, suggesting synergistic effects. The mechanism-based cell cycle progression model estimated drug interaction coefficient ψ cycle = 0.647, also suggesting synergy. Thus, FGFR inhibition appears to synergize with Gem in PDAC cells and tumors by sensitizing cells to Gem-mediated inhibition of proliferation and cell cycle progression. SIGNIFICANCE STATEMENT: An integrated approach of quantitative modeling and experimentation was employed to investigate the nature of fibroblast growth factor receptor inhibitor (FGFRi)/gemcitabine (Gem) interaction, and to identify mechanisms by which FGFRi exposure reverses Gem resistance in pancreatic cancer cells. The results show that FGFRi interacts synergistically with Gem to sensitize pancreatic cancer cells and tumors to Gem-mediated inhibition of proliferation and cell cycle progression. Thus, addition of FGFRi to standard-of-care Gem treatment could be a clinically deployable approach to enhance therapeutic benefit to pancreatic cancer patients.

Controlled coupling of an ultrapotent auristatin warhead to cetuximab yields a next-generation antibody-drug conjugate for EGFR-targeted therapy of KRAS mutant pancreatic cancer.

BACKGROUND: Antibody-drug conjugate (ADC) construction poses numerous challenges that limit clinical progress. In particular, common bioconjugation methods afford minimal control over the site of drug coupling to antibodies. Here, such difficulties are overcome through re-bridging of the inter-chain disulfides of cetuximab (CTX) with auristatin-bearing pyridazinediones, to yield a highly refined anti-epidermal growth factor receptor (EGFR) ADC. METHODS: In vitro and in vivo assessment of ADC activity was performed in KRAS mutant pancreatic cancer (PaCa) models with known resistance to CTX therapy. Computational modelling was employed for quantitative prediction of tumour response to various ADC dosing regimens. RESULTS: Site-selective coupling of an auristatin to CTX yielded an ADC with an average drug:antibody ratio (DAR) of 3.9, which elicited concentration- and EGFR-dependent cytotoxicity at sub-nanomolar potency in vitro. In human xenografts, the ADC inhibited tumour growth and prolonged survival, with no overt signs of toxicity. Key insights into factors governing ADC efficacy were obtained through a robust mathematical framework, including target-mediated dispositional effects relating to antigen density on tumour cells. CONCLUSIONS: Together, our findings offer renewed hope for CTX in PaCa therapy, demonstrating that it may be reformatted as a next-generation ADC and combined with a predictive modelling tool to guide successful translation.

Pharmacodynamic modeling of synergistic birinapant/paclitaxel interactions in pancreatic cancer cells.

BACKGROUND: For most patients, pancreatic adenocarcinoma responds poorly to treatment, and novel therapeutic approaches are needed. Standard-of-care paclitaxel (PTX), combined with birinapant (BRP), a bivalent mimetic of the apoptosis antagonist SMAC (second mitochondria-derived activator of caspases), exerts synergistic killing of PANC-1 human pancreatic adenocarcinoma cells. METHODS: To investigate potential mechanisms underlying this synergistic pharmacodynamic interaction, data capturing PANC-1 cell growth, apoptosis kinetics, and cell cycle distribution were integrated with high-quality IonStar-generated proteomic data capturing changes in the relative abundance of more than 3300 proteins as the cells responded to the two drugs, alone and combined. RESULTS: PTX alone (15 nM) elicited dose-dependent G2/M-phase arrest and cellular polyploidy. Combined BRP/PTX (150/15 nM) reduced G2/M by 35% and polyploid cells by 45%, and increased apoptosis by 20%. Whereas BRP or PTX alone produced no change in the pro-apoptotic protein pJNK, and a slight increase in the anti-apoptotic protein Bcl2, the drug combination increased pJNK and decreased Bcl2 significantly compared to the vehicle control. A multi-scale, mechanism-based mathematical model was developed to investigate integrated birinapant/paclitaxel effects on temporal profiles of key proteins involved in kinetics of cell growth, death, and cell cycle distribution. CONCLUSIONS: The model, consistent with the observed reduction in the Bcl2/BAX ratio, suggests that BRP-induced apoptosis of mitotically-arrested cells is a major contributor to the synergy between BRP and PTX. Coupling proteomic and cellular response profiles with multi-scale pharmacodynamic modeling provides a quantitative mechanistic framework for evaluating pharmacodynamically-based drug-drug interactions in combination chemotherapy, and could potentially guide the development of promising drug regimens.

Population-based meta-analysis of bortezomib exposure-response relationships in multiple myeloma patients.

Bortezomib (Velcade®) is a reversible proteasome inhibitor that shows potent antineoplastic activity, by inhibiting the constitutively increased proteasome activity in myeloma cells, and is approved as a first-line therapy for multiple myeloma (MM). Although clinically successful, bortezomib exhibits a relatively narrow therapeutic index and can induce dose-limiting toxicities such as thrombocytopenia. This study aims to develop a quantitative and predictive pharmacodynamic model to investigate bortezomib dosing-regimens in a rational and efficient manner. Mean temporal profiles of bortezomib pharmacokinetics, proteasome activity, M-protein concentrations, and platelet counts following bortezomib monotherapy were extracted from published clinical studies. A population-based meta-analysis of bortezomib anti-myeloma activity and thrombocytopenia was conducted sequentially with a Stochastic Approximation Expectation Maximization algorithm in Monolix. The final pharmacodynamic model integrates drug-target interactions and cell signaling dynamics with temporal biomarkers of clinical efficacy and toxicity. Bortezomib pharmacokinetics, disease progression, and platelet dynamic profiles were well characterized in MM patients, and a local sensitivity analysis of the final model suggests that increased proteasome concentration could ultimately attenuate bortezomib antineoplastic activity in MM patients. In addition, model simulations confirm that a once-weekly dosing schedule represents an optimal therapeutic regimen with comparable antineoplastic activity but significantly reduced risk of thrombocytopenia. In conclusion, a pharmacodynamic model was successfully developed, which provides a quantitative, mechanism-based platform for probing bortezomib dosing-regimens. Further research is needed to determine whether this model could be used to individualize bortezomib regimens to maximize antineoplastic efficacy and minimize thrombocytopenia during MM treatment.

Race/Ethnicity and Protease Inhibitor Use Influence Plasma Tenofovir Exposure in Adults Living with HIV-1 in AIDS Clinical Trials Group Study A5202.

AIDS Clinical Trial Group study A5202 (ClinicalTrials.gov identifier NCT00118898) was a phase 3b, randomized, partially blinded equivalence study of open-label atazanavir/ritonavir or efavirenz, plus either placebo-controlled tenofovir disoproxil fumarate/emtricitabine or abacavir/lamivudine, in treatment-naive adults living with HIV-1, evaluating efficacy, safety, and tolerability. We report an analysis of the contribution of participant characteristics to the disposition of tenofovir plasma concentrations. Tenofovir concentration data from a total of 817 individuals (88% of the total number of eligible patients randomly assigned to receive treatment in the TDF-containing arms of A5202) were available for analysis. Pharmacokinetic analysis was performed using nonlinear mixed-effects modeling. One- and two-compartment models with first-order absorption and first-order elimination were evaluated. An exponential error model was used for examination of interindividual variability (IIV), and a proportional and mixed-error model was assessed for residual variability. The final structural model contained two compartments with first-order absorption and elimination. IIV was estimated for apparent clearance (CL/F) and the first-order absorption rate constant (ka ), and a proportional residual variability model was selected. The final mean parameter estimates were as follows: ka = 2.87 h-1, CL/F = 37.2 liters/h, apparent volumes of the central and peripheral compartments = 127 and 646 liters, respectively, and apparent intercompartmental clearance = 107 liters/h. In addition to race/ethnicity, creatinine clearance and assignment to atazanavir/ritonavir or efavirenz were significantly associated with CL/F (P < 0.001). In conclusion, race/ethnicity is associated with tenofovir oral CL in HIV-1 positive, treatment-naive adults. This covariate relationship raises questions about the possibility of differences in efficacy and risk of adverse events in different patient populations and suggests that examining preexposure prophylaxis regimens and tenofovir exposure in different race/ethnicity groups be considered.

The impact of tacrolimus exposure on extrarenal adverse effects in adult renal transplant recipients.

AIMS: Tacrolimus has been associated with notable extrarenal adverse effects (AEs), which are unpredictable and impact patient morbidity. The association between model-predicted tacrolimus exposure metrics and standardized extrarenal AEs in stable renal transplant recipients was investigated and a limited sampling strategy (LSS) was developed to predict steady-state tacrolimus area under the curve over a 12-h dosing period (AUCss,0-12h ). METHODS: All recipients receiving tacrolimus and mycophenolic acid ≥6 months completed a 12-h cross-sectional observational pharmacokinetic-pharmacodynamic study. Patients were evaluated for the presence of individual and composite gastrointestinal, neurological, and aesthetic AEs during the study visit. The associations between AEs and tacrolimus exposure metrics generated from a published population pharmacokinetic model were investigated using a logistic regression analysis in NONMEM 7.3. An LSS was determined using a Bayesian estimation method with the same patients. RESULTS: Dose-normalized tacrolimus AUCss,0-12h and apparent clearance were independently associated with diarrhoea, dyspepsia, insomnia and neurological AE ratio. Dose-normalized tacrolimus maximum concentration was significantly correlated with skin changes and acne. No AE associations were found with trough concentrations. Using limited sampling at 0, 2h; 0, 1, 4h; and 0, 1, 2, 4h provided a precise and unbiased prediction of tacrolimus AUC (root mean squared prediction error < 10%), which was not well characterized using trough concentrations only (root mean squared prediction error >15%). CONCLUSIONS: Several AEs (i.e. diarrhoea, dyspepsia, insomnia and neurological AE ratio) were associated with tacrolimus dose normalized AUCss,0-12h and clearance. Skin changes and acne were associated with dose-normalized maximum concentrations. To facilitate clinical implementation, a LSS was developed to predict AUCss,0-12h values using sparse patient data to efficiently assess projected immunosuppressive exposure and potentially minimize AE manifestations.

Machine Learning Models for the Prediction of Chemotherapy-Induced Peripheral Neuropathy.

PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse side effect of cancer chemotherapy that can be life debilitating and cause extreme pain. The multifactorial and poorly understood mechanisms of toxicity have impeded the identification of novel treatment strategies. Computational models of drug neurotoxicity could be implemented in early drug discovery to screen for high-risk compounds and select safer drug candidates for further development. METHODS: Quantitative-structure toxicity relationship (QSTR) models were developed to predict the incidence of PN. A manually curated library of 95 approved drugs were used to develop the model. Molecular descriptors sensitive to the incidence of PN were identified to provide insights into structural modifications to reduce neurotoxicity. The incidence of PN was predicted for 60 antineoplastic drug candidates currently under clinical investigation. RESULTS: The number of aromatic nitrogens was identified as the most important molecular descriptor. The chemical transformation of aromatic nitrogens to carbons reduced the predicted PN incidence of bortezomib from 32.3% to 21.1%. Antineoplastic drug candidates were categorized into three groups (high, medium, low) based on their predicted PN incidence. CONCLUSIONS: QSTR models were developed to link physicochemical descriptors of compounds with PN incidence, which can be utilized during drug candidate selection to reduce neurotoxicity.

Network-Based Analysis of Bortezomib Pharmacodynamic Heterogeneity in Multiple Myeloma Cells.

The objective of this study is to evaluate the heterogeneity in pharmacodynamic response in four in vitro multiple myeloma cell lines to treatment with bortezomib, and to assess whether such differences are associated with drug-induced intracellular signaling protein dynamics identified via a logic-based network modeling approach. The in vitro pharmacodynamic-efficacy of bortezomib was evaluated through concentration-effect and cell proliferation dynamical studies in U266, RPMI8226, MM.1S, and NCI-H929 myeloma cell lines. A Boolean logic-based network model incorporating intracellular protein signaling pathways relevant to myeloma cell growth, proliferation, and apoptosis was developed based on information available in the literature and used to identify key proteins regulating bortezomib pharmacodynamics. The time-course of network-identified proteins was measured using the MAGPIX protein assay system. Traditional pharmacodynamic modeling endpoints revealed variable responses of the cell lines to bortezomib treatment, classifying cell lines as more sensitive (MM.1S and NCI-H929) and less sensitive (U266 and RPMI8226). Network centrality and model reduction identified key proteins (e.g., phosphorylated nuclear factor-κB, phosphorylated protein kinase B, phosphorylated mechanistic target of rapamycin, Bcl-2, phosphorylated c-Jun N-terminal kinase, phosphorylated p53, p21, phosphorylated Bcl-2-associated death promoter, caspase 8, and caspase 9) that govern bortezomib pharmacodynamics. The corresponding relative expression (normalized to 0-hour untreated-control cells) of proteins demonstrated a greater magnitude and earlier onset of stimulation/inhibition in cells more sensitive (MM.1S and NCI-H929) to bortezomib-induced cell death at 20 nM, relative to the less sensitive cells (U266 and RPMI8226). Overall, differences in intracellular signaling appear to be associated with bortezomib pharmacodynamic heterogeneity, and key proteins may be potential biomarkers to evaluate bortezomib responses.

Boolean network modeling in systems pharmacology.

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Population pharmacokinetics of exenatide.

AIM: The aim of the present analysis was to develop a core population pharmacokinetic model for the pharmacokinetic properties of immediate-release (IR) exenatide, which can be used in subsequent analyses of novel sustained-release formulations. METHODS: Data from eight clinical trials, evaluating a wide range of doses and different administration routes, were available for analysis. All modelling and simulations were conducted using the nonlinear mixed-effect modelling program NONMEM. External model validation was performed using data from the phase III clinical trials programme through standard visual predictive checks. RESULTS: The pharmacokinetics of IR exenatide was described by a two-compartment model, and the absorption of subcutaneous exenatide was described with a sequential zero-order rate constant followed by a saturable nonlinear absorption process. Drug elimination was characterized by two parallel routes (linear and nonlinear), with significant relationships between renal function and the linear elimination route, and between body weight and volume of distribution. For a subject with normal renal function, the linear clearance was estimated to be 5.06 l hr-1 . The nonlinear elimination was quantified with a Michaelis-Menten constant (Km ) of 567 pg ml-1 and a maximum rate of metabolism (Vmax ) of 1.6 µg h-1 . For subcutaneous administration, 37% of the subcutaneous dose is absorbed via the zero-order process, and the remaining 63% via the nonlinear pathway. CONCLUSIONS: The present analysis provides a comprehensive population pharmacokinetic model for exenatide, expanding the elimination process to include both linear and nonlinear components, providing a suitable platform for a broad range of concentrations and patient conditions that can be leveraged in future modelling efforts of sustained-release exenatide formulations.

Sequential Exposure of Bortezomib and Vorinostat is Synergistic in Multiple Myeloma Cells.

PURPOSE: To examine the combination of bortezomib and vorinostat in multiple myeloma cells (U266) and xenografts, and to assess the nature of their potential interactions with semi-mechanistic pharmacodynamic models and biomarkers. METHODS: U266 proliferation was examined for a range of bortezomib and vorinostat exposure times and concentrations (alone and in combination). A non-competitive interaction model was used with interaction parameters that reflect the nature of drug interactions after simultaneous and sequential exposures. p21 and cleaved PARP were measured using immunoblotting to assess critical biomarker dynamics. For xenografts, data were extracted from literature and modeled with a PK/PD model with an interaction parameter. RESULTS: Estimated model parameters for simultaneous in vitro and xenograft treatments suggested additive drug effects. The sequence of bortezomib preincubation for 24 hours, followed by vorinostat for 24 hours, resulted in an estimated interaction term significantly less than 1, suggesting synergistic effects. p21 and cleaved PARP were also up-regulated the greatest in this sequence. CONCLUSIONS: Semi-mechanistic pharmacodynamic modeling suggests synergistic pharmacodynamic interactions for the sequential administration of bortezomib followed by vorinostat. Increased p21 and cleaved PARP expression can potentially explain mechanisms of their enhanced effects, which require further PK/PD systems analysis to suggest an optimal dosing regimen.

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.

Physiologically-Based Pharmacokinetic-Pharmacodynamic Modeling of 1α,25-Dihydroxyvitamin D3 in Mice.

1α,25-Dihydroxyvitamin D3 [1,25(OH)2D3] concentrations are regulated by renal CYP27B1 for synthesis and CYP24A1 for degradation. Published plasma and tissue 1,25(OH)2D3 concentrations and mRNA fold change expression of Cyp24a1 and Cyp27b1 following repetitive i.p. injections to C57BL/6 mice (2.5 µg × kg(-1) every 2 days for 4 doses) were fitted with a minimal and full physiologically-based pharmacokinetic-pharmacodynamic models (PBPK-PD). The minimal physiologically-based pharmacokinetic-pharmacodynamic linked model (mPBPK-PD) related Cyp24a1 mRNA fold changes to linear changes in tissue/tissue baseline 1,25(OH)2D3 concentration ratios, whereas the full physiologically-based pharmacokinetic-pharmacodynamic model (PBPK-PD) related measured tissue Cyp24a1 and Cyp27b1 fold changes to tissue 1,25(OH)2D3 concentrations with indirect response, sigmoidal maximal stimulatory effect/maximal inhibitory effect functions. Moreover, the intestinal segregated flow model (SFM) that describes a low and partial intestinal (blood/plasma) flow to enterocytes was nested within both models for comparison with the traditional model for intestine (TM) where the entire flow perfuses the intestine. Both the mPBPK(SFM)-PD and full PBPK(SFM)-PD models described the i.p. plasma and tissue 1,25(OH)2D3 concentrations and fold changes in mRNA expression significantly better than the TM counterparts with F test comparisons. The full PBPK(SFM)-PD fits showed estimates with good precision (lower percentage of coefficient of variation), and the model was more robust in predicting data from escalating i.v. doses (2, 60, and 120 pmol) and the rebound in 1,25(OH)2D3 tissue concentrations after dosing termination. The full PBPK(SFM)-PD model performed the best among the tested models for describing the complex pharmacokinetic-pharmacodynamic interplay among Cyp27b1, Cyp24a1, and 1,25(OH)2D3.

Array of translational systems pharmacodynamic models of anti-cancer drugs.

Cancer is a complex disease that is characterized by an uncontrolled growth and spread of abnormal cells. Drug development in oncology is particularly challenging and is associated with one of the highest attrition rates of compounds despite substantial investments in resources. Pharmacokinetic and pharmacodynamic (PK/PD) modeling seeks to couple experimental data with mathematical models to provide key insights into factors controlling cytotoxic effects of chemotherapeutics and cancer progression. PK/PD modeling of anti-cancer compounds is equally challenging, partly based on the complexity of biological and pharmacological systems. However, reliable mechanistic and systems PK/PD models for anti-cancer agents have been developed and successfully applied to: (1) provide insights into fundamental mechanisms implicated in tumor growth, (2) assist in dose selection for first-in-human phase I studies (e.g., effective dose, escalating doses, and maximal tolerated doses), (3) design and optimize combination drug regimens, (4) design clinical trials, and (5) establish links between drug efficacy and safety and the concentrations of measured biomarkers. In this commentary, classes of relevant mechanism-based and systems PK/PD models of anti-cancer agents that have shown promise in translating preclinical data and enhancing stages of the drug development process are reviewed. Specific features of such models are discussed including their strengths and limitations along with a prospectus of using these models alone or in combination for cancer therapy.

Factor VIII associated with lipidic nanoparticles retains efficacy in the presence of anti-factor VIII antibodies in hemophilia A mice.

The development of inhibitory antibodies against factor VIII (FVIII) is a major challenge in hemophilia A (HA) therapy. Such antibodies develop in nearly 30% of patients receiving replacement FVIII, abrogating therapeutic efficacy. This work evaluated whether B-domain deleted FVIII encapsulated in phosphatidylinositol containing lipid nanoparticles (PI-BDD FVIII) could serve as an efficacious FVIII replacement therapy in the presence of inhibitors. The HA mice were given clinically relevant doses of FVIII to develop inhibitors. The efficacy of free and PI-BDD FVIII was studied in inhibitor-positive HA mice using a tail clip assay. Mathematical modeling of these data was conducted to evaluate the hypothesis that lipid association sterically shields the protein from inhibitor binding. The immunization protocol resulted in a mean inhibitory titer level of 198 ± 52 BU/ml. Free BDD FVIII was ineffective at controlling blood loss in inhibitor-positive HA mice as early as 2 h post dose. In contrast, PI-BDD FVIII treated animals retained partial hemostatic efficacy as long as 18 h post dose. Mathematical modeling supports the hypotheses that a greater fraction of lipid-associated FVIII remains unbound to inhibitors and that PI-BDD FVIII has lower binding affinity to inhibitors than does the free protein. In addition, the modeling approaches extend current efforts to model the impact of immunogenicity on PK and the therapeutically meaningful endpoint of efficacy, thereby addressing an important knowledge gap, particularly in the FVIII scientific literature. Clinical translation of these findings could result in a significant improvement in the quality of care of inhibitor-positive HA patients. Copyright © 2016 John Wiley & Sons, Ltd.

Mathematical model of platelet turnover in thrombocytopenic and nonthrombocytopenic preterm neonates.

Neonatal thrombocytopenia affects 22-35% of all neonates admitted to neonatal intensive care units. The purpose of this study was to develop a mathematical model for characterizing platelet (PLT) kinetics in thrombocytopenic preterm neonates. Immature PLT fraction (IPF) and PLT counts were measured for up to 35 days after birth in 27 very low birth weight preterm neonates. PLT transfusions were administered to 8 of the 27 (24%) subjects. The final model included a series of four transit compartments to mimic the production and survival of IPF and PLT. Model parameters were estimated using nonlinear mixed effects modeling with the maximum likelihood expectation maximization algorithm. The model adequately captured the diverse phenotypes expressed by individual subject profiles. Typical population survival values for IPF and PLT life spans in nonthrombocytopenic patients were estimated at 0.912 and 10.7 days, respectively. These values were significantly shorter in thrombocytopenic subjects, 0.429 and 2.56 days, respectively. The model was also used to evaluate the influence of growth and laboratory phlebotomy loss on the time course of IPF and PLT counts. Whereas incorporating body weight was essential to correct for expanding blood volume due to growth, phlebotomy loss, a possible covariate, did not significantly influence PLT kinetics. This study provides a platform for identifying potential covariates that influence the interindividual variability in model parameters regulating IPF and PLT kinetics and for evaluating future pharmacological therapies for treating thrombocytopenic neonates.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells.

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens.