A clinical investigation of inhibitory effect of panobinostat on CYP2D6 substrate in patients with advanced cancer

Molecular mechanisms underlying the clinical efficacy of panobinostat involve Stochasticity of epigenetic signaling, sensitization to anticancer drugs, and induction of cellular cell death related to cellular stresses

Panobinostat, also known as Farydak®, LBH589, PNB, or panobinostat lactate, is a hydroxamic acid that has been approved by the Food and Drug Administration (FDA) for its anti-cancer properties. This orally bioavailable drug is classified as a non-selective histone deacetylase inhibitor (pan-HDACi) that inhibits class I, II, and IV HDACs at nanomolar levels due to its significant histone modifications and epigenetic mechanisms. A mismatch between histone acetyltransferases (HATs) and HDACs can negatively affect the regulation of the genes concerned, which in turn can contribute to tumorigenesis. Indeed, panobinostat inhibits HDACs, potentially leading to acetylated histone accumulation, re-establishing normal gene expression in cancer cells, and helping to drive multiple signaling pathways. These pathways include induction of histone acetylation and cytotoxicity for the majority of tested cancer cell lines, increased levels of p21 cell cycle proteins, enhanced amounts of pro-apoptotic factors (such as caspase-3/7 activity and cleaved poly (ADP-ribose) polymerase (PARP)) associated with decreased levels of anti-apoptotic factors [B-cell lymphoma 2 (Bcl-2) and B-cell lymphoma-extra-large (Bcl-XL)], as well as regulation of immune response [upregulated programmed death-ligand 1 (PD-L1) and interferon gamma receptor 1 (IFN-γR1) expression] and other events. The therapeutic outcome of panobinostat is therefore mediated by sub-pathways involving proteasome and/or aggresome degradation, endoplasmic reticulum, cell cycle arrest, promotion of extrinsic and intrinsic processes of apoptosis, tumor microenvironment remodeling, and angiogenesis inhibition. In this investigation, we aimed to pinpoint the precise molecular mechanism underlying panobinostat's HDAC inhibitory effect. A more thorough understanding of these mechanisms will greatly advance our knowledge of cancer cell aberrations and, as a result, provide an opportunity for the discovery of significant new therapeutic perspectives through cancer therapeutics.

Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

This study provides a whole‐body physiologically‐based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O‐glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug‐gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent‐metabolite‐metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O‐glucuronide was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Plasma concentration‐time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration‐time profiles, area under the concentration‐time curve from the time of the first measurement to the time of the last measurement (AUC_last) and maximum concentration (C_max) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration‐time profiles and plasma concentration‐time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUC_last and seven of seven DGI C_max ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 k_cat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log‐normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.

Clinical pharmacokinetics of oral drugs in the treatment of multiple myeloma

Treatment of myeloma is a long-term treatment mainly based on all-oral combinations of drugs. Because oral drugs have a more complex pharmacokinetics compared with IV treatments, an appropriate knowledge of the factors that may alter their systemic exposure is of particular clinical relevance. Both drug-drug interactions, food-effect, and dose-adaptation in renal and hepatic impairment may influence the systemic drug levels with a potential impact on drug efficacy or safety. Moreover, a better control of drug exposure may improve the side effect profiles of these treatments with a favourable impact on patient compliance. Furthermore, as long-term treatments, these drugs may also alter the systemic exposure of coadministered medications in these rather old patients. The aim of this review was to identify the factors modifying the systemic exposure of oral drugs used in myeloma by focusing on the pharmacokinetic drug-drug interactions and the effects of renal and hepatic impairment and of food impact.

Application of physiologically based pharmacokinetic modeling in predicting drug-drug interactions for sarpogrelate hydrochloride in humans

Background

Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important.

Objective

To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine.

Methods

The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol.

Results

The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition.

Conclusion

This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.

Panobinostat as Pan-deacetylase Inhibitor for the Treatment of Pancreatic Cancer: Recent Progress and Future Prospects

The histone deacetylase (HDAC) inhibitors have been demonstrated as an emerging class of anticancer drugs. HDACs are involved in regulation of gene expression and in chromatin remodeling, thus indicating valid targets for different types of cancer therapeutics. The pan-deacetylase inhibitor panobinostat (Farydac®, LBH589) was developed by Novartis Pharmaceuticals and has been recently approved by the US Food and Drug Administraion (FDA) as a drug to treat multiple myeloma. It is under clinical investigation for a range of haematological and solid tumors worldwide in both oral and intravenous formulations. Panobinostat inhibits tumor cell growth by interacting with acetylation of histones and non-histone proteins as well as various apoptotic, autophagy-mediated targets and various tumorogenesis pathways involved in development of tumors. The optimal combination regimen for pancreatic cancer remains to be fully elucidated with various combination regimens, and should be investigated in clinical trials. This article summarizes the current preclinical and clinical status of panobinostat in pancreatic cancer. Preclinical data suggests that panobinostat has potential inhibitory activity in pancreatic cancer cells by targeting various pathways and factors involved in the development of cancer. Herein, we reviewed the status of mono and combination therapy and the rationale behind the combination therapy undergoing trials, as well as possible future prospective use in the treatment of pancreatic cancer.

Clinical pharmacokinetics of panobinostat, a novel histone deacetylase (HDAC) inhibitor: review and perspectives

1. Panobinostat is a recently approved histone deacetylase (HDAC) inhibitor. 2. The pharmacokinetic data of panobinostat in patients with hematologic malignancies and advanced solid tumors have been collated and reviewed from the various published clinical studies for over a decade. Further perspectives and anticipated challenges in the clinical therapy with panobinostat are discussed in the review. 3. Regardless of intravenous or oral dosing, panobinostat showed a high degree of inter-patient variability in the pharmacokinetics. After oral administration, most of the administered dose is extensively metabolized and the metabolites are either fecally or renally excreted with trace amount of intact panobinostat. Both cytochrome p450 (CYP) 3A4 and non-CYP mechanisms govern the clearance of panobinostat. CYP3A4-related drug-drug interactions with panobinostat have been documented with ketoconazole (inhibitor) and dexamethasone (inducer). 4. In summary, the clinical pharmacokinetic data of panobinostat, a promising HDAC inhibitor, obtained from various clinical studies do not appear to limit the utility of panobinostat in the clinic.

Panobinostat PK/PD profile in combination with bortezomib and dexamethasone in patients with relapsed and relapsed/refractory multiple myeloma

PURPOSE

Panobinostat, a potent pan-deacetylase inhibitor, improved progression-free survival (PFS) in patients with relapsed and refractory multiple myeloma when combined with bortezomib and dexamethasone in a phase 3 trial, PANORAMA-1. This study aims to explore exposure-response relationship for panobinostat in this combination in a phase 1 trial, B2207 and contrast with data from historical single-agent studies.

METHODS

Panobinostat plasma concentration-time profiles were obtained in patients from PANORAMA-1 (n = 12) and B2207 (n = 12) trials. Overall response rates (ORR) and major adverse events (AE) by panobinostat exposure were investigated in the B2207 trial. Panobinostat PK data from combination trials were contrasted with data from single-agent studies.

RESULTS

At maximum tolerated dose (MTD), the geometric mean of panobinostat area under curve from 0 to 24 h (AUC0-24) was 47.5 ng h/mL (77 % CV), and maximum plasma concentration (Cmax) was 8.1 ng/mL (90 % CV). These values were comparable with exposure data obtained in PANORAMA-1, but were 20 % lower than those without dexamethasone, and ∼ 50 % lower from single-agent trials, likely due to enzyme induction by dexamethasone. Higher levels of panobinostat exposure were associated with higher response rates and higher incidences of diarrhea and thrombocytopenia.

CONCLUSIONS

Apparent panobinostat exposure-AE and exposure-ORR relationships were observed when combined with bortezomib and dexamethasone in the treatment of patients with relapsed and refractory multiple myeloma. The addition of dexamethasone facilitated best response even though plasma exposure of panobinostat was reduced. Combination with a strong enzyme inducer should be avoided in future trials to prevent further reduction of panobinostat exposure.

Population pharmacokinetics of intravenous and oral panobinostat in patients with hematologic and solid tumors

PURPOSE

The study aimed to characterize the population pharmacokinetics of panobinostat, a pan-deacetylase inhibitor that has demonstrated efficacy in combination with bortezomib and dexamethasone in patients with multiple myeloma.

METHODS

A nonlinear mixed-effect model was used to fit plasma panobinostat concentration-time data collected from patients across 14 phase 1 and phase 2 trials following either oral or intravenous (IV) administration. The model was used to estimate bioavailabilities of the two oral formulations and the effects of demographic and clinical covariates on the central volume of distribution and clearance of panobinostat.

RESULTS

A total of 7834 samples from 581 patients were analyzed. Panobinostat pharmacokinetic parameters were best characterized by a three-compartment model with first-order absorption and elimination. Bioavailability was 21.4 %. Median clearance was 33.1 L/h. Interindividual variability in clearance was 74 %. For Caucasian patients of median age 61 years, area under the curve (AUC) decreased from 104 to 88 ng · h/mL as body surface area (BSA) increased from the first to third quartiles, 1.8 to 2.1 m(2). For Caucasian patients of median BSA 1.9 m(2), AUC decreased from 102 to 95 ng · h/mL as age increased from the first to third quartiles, 51 to 70 years. For patients of median BSA and median age, AUC ranged across the four race categories from 80 to 116 ng · h/mL. Covariate analysis showed no impact on panobinostat clearance and volume by patients' sex, tumor type, kidney function, liver markers, or coadministered medications. However, separate analyses of dedicated studies have demonstrated effects of liver impairment and CYP3A4 inhibition.

CONCLUSIONS

Although covariate analyses revealed significant effects of body size, age, and race on panobinostat pharmacokinetics, these effects were minor compared to the interindividual variability and therefore not clinically relevant when dosing panobinostat in populations similar to those studied.

Effect of the potent CYP2D6 inhibitor sarpogrelate on the pharmacokinetics and pharmacodynamics of metoprolol in healthy male Korean volunteers

1. Recently, we demonstrated that sarpogrelate is a potent and selective CYP2D6 inhibitor in vitro. Here, we evaluated the effect of sarpogrelate on the pharmacokinetics and pharmacodynamics of metoprolol in healthy subjects. 2. Nine healthy male subjects genotyped for CYP2D6*1/*1 or *1/*2 were included in an open-label, randomized, three treatment-period and crossover study. A single oral dose of metoprolol (100 mg) was administered with water (treatment A) and sarpogrelate (100 mg bid.; a total dose of 200 mg and treatment B), or after pretreatment of sarpogrelate for three days (100 mg tid.; treatment C). Plasma levels of metoprolol and α-hydroxymetoprolol were determined using a validated LC-MS/MS method. Changes in heart rate and blood pressure were monitored as pharmacodynamic responses to metoprolol. 3. Metoprolol was well tolerated in the three treatment groups. In treatment B and C groups, the AUCt of metoprolol increased by 53% (GMR, 1.53; 90% CI, 1.17-2.31) and by 51% (1.51; 1.17-2.31), respectively. Similar patterns were observed for the increase in Cmax of metoprolol by sarpogrelate. However, the pharmacodynamics of metoprolol did not differ significantly among the three treatment groups. 4. Greater systemic exposure to metoprolol after co-administration or pretreatment with sarpogrelate did not result in clinically relevant effects. Co-administration of both agents is well tolerated and can be employed without the need for dose adjustments.