Drug-drug interaction studies of cardiovascular drugs involving P-glycoprotein, an efflux transporter, on the pharmacokinetics of edoxaban, an oral factor Xa inhibitor.

BACKGROUND: Edoxaban, an oral direct factor Xa inhibitor, is in development for thromboprophylaxis, including prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). P-glycoprotein (P-gp), an efflux transporter, modulates absorption and excretion of xenobiotics. Edoxaban is a P-gp substrate, and several cardiovascular (CV) drugs have the potential to inhibit P-gp and increase drug exposure. OBJECTIVE: To assess the potential pharmacokinetic interactions of edoxaban and 6 cardiovascular drugs used in the management of AF and known P-gp substrates/inhibitors. METHODS: Drug-drug interaction studies with edoxaban and CV drugs with known P-gp substrate/inhibitor potential were conducted in healthy subjects. In 4 crossover, 2-period, 2-treatment studies, subjects received edoxaban 60 mg alone and coadministered with quinidine 300 mg (n = 42), verapamil 240 mg (n = 34), atorvastatin 80 mg (n = 32), or dronedarone 400 mg (n = 34). Additionally, edoxaban 60 mg alone and coadministered with amiodarone 400 mg (n = 30) or digoxin 0.25 mg (n = 48) was evaluated in a single-sequence study and 2-cohort study, respectively. RESULTS: Edoxaban exposure measured as area under the curve increased for concomitant administration of edoxaban with quinidine (76.7 %), verapamil (52.7 %), amiodarone (39.8 %), and dronedarone (84.5 %), and exposure measured as 24-h concentrations for quinidine (11.8 %), verapamil (29.1 %), and dronedarone (157.6 %) also increased. Administration of edoxaban with amiodarone decreased the 24-h concentration for edoxaban by 25.7 %. Concomitant administration with digoxin or atorvastatin had minimal effects on edoxaban exposure. CONCLUSION: Coadministration of the P-gp inhibitors quinidine, verapamil, and dronedarone increased edoxaban exposure. Modest/minimal effects were observed for amiodarone, atorvastatin, and digoxin.

Efficacy of Pharmacokinetics-Directed Busulfan, Cyclophosphamide, and Etoposide Conditioning and Autologous Stem Cell Transplantation for Lymphoma: Comparison of a Multicenter Phase II Study and CIBMTR Outcomes.

Busulfan, cyclophosphamide, and etoposide (BuCyE) is a commonly used conditioning regimen for autologous stem cell transplantation (ASCT). This multicenter, phase II study examined the safety and efficacy of BuCyE with individually adjusted busulfan based on preconditioning pharmacokinetics. The study initially enrolled Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) patients ages 18 to 80 years but was amended due to high early treatment-related mortality (TRM) in patients > 65 years. BuCyE outcomes were compared with contemporaneous recipients of carmustine, etoposide, cytarabine, and melphalan (BEAM) from the Center for International Blood and Marrow Transplant Research. Two hundred seven subjects with HL (n = 66) or NHL (n = 141) were enrolled from 32 centers in North America, and 203 underwent ASCT. Day 100 TRM for all subjects (n = 203), patients > 65 years (n = 17), and patients ≤ 65 years (n = 186) were 4.5%, 23.5%, and 2.7%, respectively. The estimated rates of 2-year progression-free survival (PFS) were 33% for HL and 58%, 77%, and 43% for diffuse large B cell lymphoma (DLBCL; n = 63), mantle cell lymphoma (MCL; n = 29), and follicular lymphoma (FL; n = 23), respectively. The estimated rates of 2-year overall survival (OS) were 76% for HL and 65%, 89%, and 89% for DLBCL, MCL, and FL, respectively. In the matched analysis rates of 2-year TRM were 3.3% for BuCyE and 3.9% for BEAM, and there were no differences in outcomes for NHL. Patients with HL had lower rates of 2-year PFS with BuCyE, 33% (95% CI, 21% to 46%), than with BEAM, 59% (95% CI, 52% to 66%), with no differences in TRM or OS. BuCyE provided adequate disease control and safety in B cell NHL patients ≤ 65 years but produced worse PFS in HL patients when compared with BEAM.

Safety, tolerability, and mechanisms of antiretroviral activity of pegylated interferon Alfa-2a in HIV-1-monoinfected participants: a phase II clinical trial.

BACKGROUND: To our knowledge, the antiviral activity of pegylated interferon alfa-2a has not been studied in participants with untreated human immunodeficiency virus type 1 (HIV-1) infection but without chronic hepatitis C virus (HCV) infection. METHODS: Untreated HIV-1-infected volunteers without HCV infection received 180 microg of pegylated interferon alfa-2a weekly for 12 weeks. Changes in plasma HIV-1 RNA load, CD4(+) T cell counts, pharmacokinetics, pharmacodynamic measurements of 2',5'-oligoadenylate synthetase (OAS) activity, and induction levels of interferon-inducible genes (IFIGs) were measured. Nonparametric statistical analysis was performed. RESULTS: Eleven participants completed 12 weeks of therapy. The median plasma viral load decrease and change in CD4(+) T cell counts at week 12 were 0.61 log(10) copies/mL (90% confidence interval [CI], 0.20-1.18 log(10) copies/mL) and -44 cells/microL (90% CI, -95 to 85 cells/microL), respectively. There was no correlation between plasma viral load decreases and concurrent pegylated interferon plasma concentrations. However, participants with larger increases in OAS level exhibited greater decreases in plasma viral load at weeks 1 and 2 (r = -0.75 [90% CI, -0.93 to -0.28] and r = -0.61 [90% CI, -0.87 to -0.09], respectively; estimated Spearman rank correlation). Participants with higher baseline IFIG levels had smaller week 12 decreases in plasma viral load (0.66 log(10) copies/mL [90% CI, 0.06-0.91 log(10) copies/mL]), whereas those with larger IFIG induction levels exhibited larger decreases in plasma viral load (-0.74 log(10) copies/mL [90% CI, -0.93 to -0.21 log(10) copies/mL]). CONCLUSION: Pegylated interferon alfa-2a was well tolerated and exhibited statistically significant anti-HIV-1 activity in HIV-1-monoinfected patients. The anti-HIV-1 effect correlated with OAS protein levels (weeks 1 and 2) and IFIG induction levels (week 12) but not with pegylated interferon concentrations.

Pharmacogenomics in early-phase clinical development.

Pharmacogenomics (PGx) offers the promise of utilizing genetic fingerprints to predict individual responses to drugs in terms of safety, efficacy and pharmacokinetics. Early-phase clinical trial PGx applications can identify human genome variations that are meaningful to study design, selection of participants, allocation of resources and clinical research ethics. Results can inform later-phase study design and pipeline developmental decisions. Nevertheless, our review of the clinicaltrials.gov database demonstrates that PGx is rarely used by drug developers. Of the total 323 trials that included PGx as an outcome, 80% have been conducted by academic institutions after initial regulatory approval. Barriers for the application of PGx are discussed. We propose a framework for the role of PGx in early-phase drug development and recommend PGx be universally considered in study design, result interpretation and hypothesis generation for later-phase studies, but PGx results from underpowered studies should not be used by themselves to terminate drug-development programs.

Effect of ritonavir-induced cytochrome P450 3A4 inhibition on plasma fentanyl concentrations during patient-controlled epidural labor analgesia: a pharmacokinetic simulation.

BACKGROUND: Ritonavir inhibition of cytochrome P450 3A4 decreases the elimination clearance of fentanyl by 67%. We used a pharmacokinetic model developed from published data to simulate the effect of sample patient-controlled epidural labor analgesic regimens on plasma fentanyl concentrations in the absence and presence of ritonavir-induced cytochrome P450 3A4 inhibition. METHODS: Fentanyl absorption from the epidural space was modeled using tanks-in-series delay elements. Systemic fentanyl disposition was described using a three-compartment pharmacokinetic model. Parameters for epidural drug absorption were estimated by fitting the model to reported plasma fentanyl concentrations measured after epidural administration. The validity of the model was assessed by comparing predicted plasma concentrations after epidural administration to published data. The effect of ritonavir was modeled as a 67% decrease in fentanyl elimination clearance. Plasma fentanyl concentrations were simulated for six sample patient-controlled epidural labor analgesic regimens over 24 h using ritonavir and control models. Simulated data were analyzed to determine if plasma fentanyl concentrations producing a 50% decrease in minute ventilation (6.1 ng/mL) were achieved. RESULTS: Simulated plasma fentanyl concentrations in the ritonavir group were higher than those in the control group for all sample labor analgesic regimens. Maximum plasma fentanyl concentrations were 1.8 ng/mL and 3.4 ng/mL for the normal and ritonavir simulations, respectively, and did not reach concentrations associated with 50% decrease in minute ventilation. CONCLUSION: Our model predicts that even with maximal clinical dosing regimens of epidural fentanyl over 24 h, ritonavir-induced cytochrome P450 3A4 inhibition is unlikely to produce plasma fentanyl concentrations associated with a decrease in minute ventilation.

Design, Synthesis, and Biological Characterization of Largazole Analogues

Histone deacetylases (HDACs) have been shown to play key roles in tumorigenesis, and

have been validated as effective enzyme target for cancer treatment. Largazole, a marine natural

product isolated from the cyanobacterium Symploca, is an extremely potent HDAC inhibitor that

has been shown to possess high differential cytotoxicity towards cancer cells along with excellent

HDAC class-selectivity. However, improvements can be made in the isoform-selectivity and

pharmacokinetic properties of largazole.

In attempts to make these improvements and furnish a more efficient biochemical probe

as well as a potential therapeutic, several largazole analogues have been designed, synthesized,

and tested for their biological activity. Three different types of analogues were prepared. First,

different chemical functionalities were introduced at the C2 position to probe the class Iselectivity profile of largazole. Additionally, docking studies led to the design of a potential

HDAC8-selective analogue. Secondly, the thiol moiety in largazole was replaced with a wide

variety of othe zinc-binding group in order to probe the effect of Zn2+ affinity on HDAC

inhibition. Lastly, three disulfide analogues of largazole were prepared in order to utilize a

different prodrug strategy to modulate the pharmacokinetic properties of largazole.

Through these analogues it was shown that C2 position can be modified significantly

without a major loss in activity while also eliciting minimal changes in isoform-selectivity. While

the Zn2+-binding group plays a major role in HDAC inhibition, it was also shown that the thiol

can be replaced by other functionalities while still retaining inhibitory activity. Lastly, the use of

a disulfide prodrug strategy was shown to affect pharmacokinetic properties resulting in varying

functional responses in vitro and in vivo.

v

Largazole is already an impressive HDAC inhibitor that shows incredible promise.

However, in order to further develop this natural product into an anti-cancer therapeutic as well as

a chemical probe, improvements in the areas of pharmacokinetics as well as isoform-selectivity

are required. Through these studies we plan on building upon existing structure–activity

relationships to further our understanding of largazole’s mechanism of inhibition so that we may

improve these properties and ultimately develop largazole into an efficient HDAC inhibitor that

may be used as an anti-cancer therapeutic as well as a chemical probe for the studying of

biochemical systems.

On the Advancement of Probabilistic Models of Decompression Sickness

The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.

The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.

We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.

Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.