A unified framework for weighted parametric group sequential design.

Group sequential design (GSD) is widely used in clinical trials in which correlated tests of multiple hypotheses are used. Multiple primary objectives resulting in tests with known correlations include evaluating (1) multiple experimental treatment arms, (2) multiple populations, (3) the combination of multiple arms and multiple populations, or (4) any asymptotically multivariate normal tests. In this paper, we focus on the first three of these and extend the framework of the weighted parametric multiple test procedure from fixed designs with a single analysis per objective to a GSD setting where different objectives may be assessed at the same or different times, each in a group sequential fashion. Pragmatic methods for design and analysis of weighted parametric group sequential design under closed testing procedures are proposed to maintain the strong control of the family-wise Type I error rate when correlations between tests are incorporated. This results in the ability to relax testing bounds compared to designs not fully adjusting for known correlations, increasing power, or allowing decreased sample size. We illustrate the proposed methods using clinical trial examples and conduct a simulation study to evaluate the operating characteristics.

In Vitro Effects of Paclitaxel and Cremophor EL on Human Riboflavin Transporter SLC52A2.

Paclitaxel, a mitotic inhibitor with anti-cancer effects, is dissolved in Cremophor EL (CrEL). However, peripheral neuropathy is a known side effect. As one of the mechanisms of the neuropathy, mitochondrial dysfunction has been proposed, while peroxidation products are involved in the cause of CrEL-induced neurotoxicity. Riboflavin is an essential nutrient required for ATP production in mitochondria and has an antioxidant role as a coenzyme for glutathione. Therefore, riboflavin transporters might play a key role to mitigate neuropathy. However, it is unclear whether paclitaxel and CrEL affect these transporters. In this study, human riboflavin transporter SLC52A2 was used to analyze the effects of paclitaxel and CrEL. CrEL, but not paclitaxel, inhibited uptake of riboflavin in human embryonic kidney 293 cells transfected with the SLC52A2 expression vector, suggesting that altered riboflavin disposition may be involved in the pathogenesis of paclitaxel/CrEL toxicity.

Bayesian optimal phase II clinical trial design with time-to-event endpoint.

We propose a Bayesian optimal phase II (BOP2) design for clinical trials with a time-to-event endpoint (eg, progression-free survival [PFS]) or co-primary endpoints consisted of a time-to-event endpoint and a categorical endpoint (eg, PFS and toxicity). We use an exponential-inverse gamma model to model the time to event. At each interim, the go/no-go decision is made by comparing the posterior probabilities of the event of interest with an adaptive probability cutoff. The BOP2 design is flexible in the number of interim looks and applicable to both single-arm and two-arm trials. The design maximizes the power for detecting effective treatments, with a well-controlled type I error, thereby bridging the gap between Bayesian designs and frequentist designs. The BOP2 design is easy to implement. Its stopping boundary can be enumerated and included in study protocol before the onset of the trial for single-arm studies. Simulation studies show that the BOP2 design has favorable operating characteristics, with higher power and lower risk of incorrectly terminating the trial than some Bayesian phase II designs. The software to implement the BOP2 design will be freely available at www.trialdesign.org.

Defining the efficacy of neurokinin-1 receptor antagonists in controlling chemotherapy-induced nausea and vomiting in different emetogenic settings-a meta-analysis.

PURPOSE: This meta-analysis was performed to evaluate the efficacy of neurokinin-1 receptor antagonists (NK1RAs) for the prevention of chemotherapy-induced nausea and vomiting (CINV) across different categories of chemotherapeutic emetogenicity. METHODS: A systematic review of MEDLINE (via PubMed) and OVID databases, plus major oncology conferences, identified randomized, controlled trials evaluating NK1RAs in combination with a 5-HT3 RA plus a glucocorticoid for management of CINV. Efficacy end points were no emesis, no nausea, and complete response (CR) rates. Data were analyzed using a random effects model. RESULTS: Twenty-three trials (N = 11,814) were identified. Based on absolute differences (AD) for no emesis (21 %), no nausea (8 %), CR (16 %), and odd ratios (OR) of 2.62, 1.43, and 2.16, respectively, NK1RA regimens provided better CINV protection versus control groups (all p < 0.00001) in patients receiving cisplatin-based highly emetogenic chemotherapy (HEC). In patients receiving anthracycline/cyclophosphamide (AC)-based HEC, respective ADs and ORs were 14, 4, and 11 % and 1.97 (p < 0.0001), 1.17 (p = 0.04), and 1.62 (p < 0.00001). In patients receiving moderately emetogenic chemotherapy (3 trials), no statistically significant benefit of NK1RAs was found; however, positive trends were detected for CR and no emesis. NK1RAs were effective for CINV prevention in a small number of studies using high-dose chemotherapy as conditioning prior to stem cell transplant and cisplatin-based multiple-day chemotherapy (MDC). CONCLUSIONS: This meta-analysis demonstrated the efficacy of NK1RA in preventing vomiting in patients receiving HEC (including AC), with smaller effects on prevention of nausea. Efficacy is also seen with high-dose chemotherapy and cisplatin-based MDC.

Vorinostat or placebo in combination with bortezomib in patients with multiple myeloma (VANTAGE 088): a multicentre, randomised, double-blind study.

BACKGROUND: We aimed to assess efficacy and tolerability of vorinostat in combination with bortezomib for treatment of patients with relapsed or refractory multiple myeloma. METHODS: In our randomised, double-blind, placebo-controlled, phase 3 trial, we enrolled adults (≥18 years) at 174 university hospitals in 31 countries worldwide. Eligible patients had to have non-refractory multiple myeloma that previously responded to treatment (one to three regimens) but were currently progressing, ECOG performance statuses of 2 or less, and no continuing toxic effects from previous treatment. We excluded patients with known resistance to bortezomib. We randomly allocated patients (1:1) using an interactive voice response system to receive 21 day cycles of bortezomib (1·3 mg/m(2) intravenously on days 1, 4, 8, and 11) in combination with oral vorinostat (400 mg) or matching placebo once-daily on days 1-14. We stratified patients by baseline tumour stage (International Staging System stage 1 or stage ≥2), previous bone-marrow transplantation (yes or no), and number of previous regimens (1 or ≥2). The primary endpoint was progression-free survival (PFS) in the intention-to-treat population. We assessed adverse events in all patients who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, number 00773747. FINDINGS: Between Dec 24, 2008, and Sept 8, 2011, we randomly allocated 317 eligible patients to the vorinostat group (315 of whom received at least one dose) and 320 to the placebo group (all of whom received at least one dose). Median PFS was 7·63 months (95% CI 6·87-8·40) in the vorinostat group and 6·83 months (5·67-7·73) in the placebo group (hazard ratio [HR] 0·77, 95% CI 0·64-0·94; p=0·0100). 312 (99%) of 315 patients in the vorinostat group and 315 (98%) of 320 patients in the placebo group had adverse events (300 [95%] adverse events in the vorinostat group and 282 [88%] in the control group were regarded as related to treatment). The most common grade 3-4 adverse events were thrombocytopenia (143 [45%] patients in the vorinostat group vs 77 [24%] patients in the placebo group), neutropenia (89 [28%] vs 80 [25%]), and anaemia (53 [17%] vs 40 [13%]). INTERPRETATION: Although the combination of vorinostat and bortezomib prolonged PFS relative to bortezomib and placebo, the clinical relevance of the difference in PFS between the two groups is not clear. Different treatment schedules of bortezomib and vorinostat might improve tolerability and enhance activity. FUNDING: Merck.

The poly(ADP-ribose) polymerase inhibitor niraparib (MK4827) in BRCA mutation carriers and patients with sporadic cancer: a phase 1 dose-escalation trial.

BACKGROUND: Poly(ADP-ribose) polymerase (PARP) is implicated in DNA repair and transcription regulation. Niraparib (MK4827) is an oral potent, selective PARP-1 and PARP-2 inhibitor that induces synthetic lethality in preclinical tumour models with loss of BRCA and PTEN function. We investigated the safety, tolerability, maximum tolerated dose, pharmacokinetic and pharmacodynamic profiles, and preliminary antitumour activity of niraparib. METHODS: In a phase 1 dose-escalation study, we enrolled patients with advanced solid tumours at one site in the UK and two sites in the USA. Eligible patients were aged at least 18 years; had a life expectancy of at least 12 weeks; had an Eastern Cooperative Oncology Group performance status of 2 or less; had assessable disease; were not suitable to receive any established treatments; had adequate organ function; and had discontinued any previous anticancer treatments at least 4 weeks previously. In part A, cohorts of three to six patients, enriched for BRCA1 and BRCA2 mutation carriers, received niraparib daily at ten escalating doses from 30 mg to 400 mg in a 21-day cycle to establish the maximum tolerated dose. Dose expansion at the maximum tolerated dose was pursued in 15 patients to confirm tolerability. In part B, we further investigated the maximum tolerated dose in patients with sporadic platinum-resistant high-grade serous ovarian cancer and sporadic prostate cancer. We obtained blood, circulating tumour cells, and optional paired tumour biopsies for pharmacokinetic and pharmacodynamic assessments. Toxic effects were assessed by common toxicity criteria and tumour responses ascribed by Response Evaluation Criteria in Solid Tumors (RECIST). Circulating tumour cells and archival tumour tissue in prostate patients were analysed for exploratory putative predictive biomarkers, such as loss of PTEN expression and ETS rearrangements. This trial is registered with ClinicalTrials.gov, NCT00749502. FINDINGS: Between Sept 15, 2008, and Jan 14, 2011, we enrolled 100 patients: 60 in part A and 40 in part B. 300 mg/day was established as the maximum tolerated dose. Dose-limiting toxic effects reported in the first cycle were grade 3 fatigue (one patient given 30 mg/day), grade 3 pneumonitis (one given 60 mg/day), and grade 4 thrombocytopenia (two given 400 mg/day). Common treatment-related toxic effects were anaemia (48 patients [48%]), nausea (42 [42%]), fatigue (42 [42%]), thrombocytopenia (35 [35%]), anorexia (26 [26%]), neutropenia (24 [24%]), constipation (23 [23%]), and vomiting (20 [20%]), and were predominantly grade 1 or 2. Pharmacokinetics were dose proportional and the mean terminal elimination half-life was 36·4 h (range 32·8-46·0). Pharmacodynamic analyses confirmed PARP inhibition exceeded 50% at doses greater than 80 mg/day and antitumour activity was documented beyond doses of 60 mg/day. Eight (40% [95% CI 19-64]) of 20 BRCA1 or BRCA2 mutation carriers with ovarian cancer had RECIST partial responses, as did two (50% [7-93]) of four mutation carriers with breast cancer. Antitumour activity was also reported in sporadic high-grade serous ovarian cancer, non-small-cell lung cancer, and prostate cancer. We recorded no correlation between loss of PTEN expression or ETS rearrangements and measures of antitumour activity in patients with prostate cancer. INTERPRETATION: A recommended phase 2 dose of 300 mg/day niraparib is well tolerated. Niraparib should be further assessed in inherited and sporadic cancers with homologous recombination DNA repair defects and to target PARP-mediated transcription in cancer. FUNDING: Merck Sharp and Dohme.

Evaluation of early efficacy endpoints for proof-of-concept trials.

A Phase II proof-of-concept (POC) trial usually uses an early efficacy endpoint other than a clinical endpoint as the primary endpoint. Because of the advancement in bioscience and technology, which has yielded a number of new surrogate biomarkers, drug developers often have more candidate endpoints to choose from than they can handle. As a result, selection of endpoint and its effect size as well as choice of type I/II error rates are often at the center of heated debates in design of POC trials. While optimization of the trade-off between benefit and cost is the implicit objective in such a decision-making process, it is seldom explicitly accounted for in practice. In this research note, motivated by real examples from the oncology field, we provide practical measures for evaluation of early efficacy endpoints (E4) for POC trials. We further provide optimal design strategies for POC trials that include optimal Go-No Go decision criteria for initiation of Phase III and optimal resource allocation strategies for conducting multiple POC trials in a portfolio under fixed resources. Although oncology is used for illustration purpose, the same idea developed in this research note also applies to similar situations in other therapeutic areas or in early-stage drug development in that a Go-No Go decision has to rely on limited data from an early efficacy endpoint and cost-effectiveness is the main concern.

Framing the statistical criteria for accelerated approval of oncology drugs: A pathway for front runners.

FDA has recently proposed a one-trial approach in Project FrontRunner for both accelerated approval and regular approval in the same trial. The goal of this short communication is to make the regulatory criteria for decision-making on accelerated approval based on the interim analysis more explicit. Two related high-level statistical issues are addressed under simplified yet representative scenarios. The first is at what Type I error level should the interim analysis be tested? To conform with the conventional two-trial approach, it may be tested at the 1.0% level without paying penalty for regular approval. This criterion is more relaxed than current practice. The second is what may be considered as adequate totality of evidence for accelerated approval. It turns out that, with the first criterion met, the observed treatment effect of the clinical endpoint intended for regular approval needs to be close to the target effect for the study. The criterion helps make the regulatory decision more transparent and objective.

Statistical considerations of designs for single-arm proof-of-concept oncology trial with time-to-event endpoint.

Single-arm proof-of-concept (PoC) clinical studies are widely used to accelerate the signal-finding process in oncology drug development before or in lieu of randomized PoC studies. Traditionally the primary endpoint for single-arm PoC studies is objective response rate (ORR). However, in cases that ORR is not applicable or not clinically relevant, time-to-event (TTE) endpoint is used instead. One conventional approach is to dichotomize the TTE endpoint as a binary endpoint to assess the survival rate, which may compromise the testing efficiency due to the requirement of minimum follow-up without censoring. Alternatively, we can use the non-parametric one-sample log-rank test (OSLRT) to evaluate the survival curve difference compared with historical control. This approach can incorporate censoring and all time-point information on the survival curve, but the test statistic may be difficult to interpret and quantify the magnitude of treatment effect. Given that clinicians are more interested in the survival rate at a clinically relevant landmark timepoint, we can also use a landmark Kaplan-Meier method (LMKM) to estimate the survival rate at a landmark timepoint for design and analysis of single-arm proof-of-concept oncology trials with TTE endpoint. This non-parametric method is straightforward to clinicians and can be applied to any survival models. Simulation studies show that the LMKM method can improve the efficiency upon the binary survival rate approach and achieve comparable operating characteristics as the one-sample log-rank test. We also develop an R package for the implementation of these mainstream designs, which fills the gap of available software for design and analysis of single-arm studies with TTE endpoint.

A promising biomarker adaptive Phase 2/3 design - Explained and expanded.

This short communication concerns a biomarker adaptive Phase 2/3 design for new oncology drugs with an uncertain biomarker effect. Depending on the outcome of an interim analysis for adaptive decision, a Phase 2 study that starts in a biomarker enriched subpopulation may continue to the end without expansion to Phase 3, expand to Phase 3 in the same population or expand to Phase 3 in a broader population. Each path can enjoy full alpha for hypothesis testing without inflating the overall Type I error.

Statistical Interpretation and Comparison of Waterfall Plots.

Waterfall plots have gained popularity as a visualization tool to present antitumor activity of treatments in oncology, especially for phase I and II trials. The typical waterfall plot in oncology is a bar plot with each bar representing the best percent tumor size reduction from baseline for a patient sorted in descending order along the x-axis. As new therapies are routinely developed in combination with standard of care or other investigational treatments, waterfall plot comparison between combination therapy and monotherapy may facilitate development decisions in addition to overall response rate or duration of response. However, waterfall plots are often assessed heuristically in practice with lack of statistical rigor. In this work, we examine the correspondence between the waterfall plot and the empirical cumulative distribution function. We demonstrate how to derive key summary statistics directly from the waterfall plot. Using real examples from published waterfall plots, we show how comparisons of waterfall plots can elucidate clinically meaningful information, such as treatment effect patterns in progression-free survival and overall survival.

Statistical Considerations on the Use of RWD/RWE for Oncology Drug Approvals: Overview and Lessons Learned.

Despite increasing utilization of real-world data (RWD)/real-world evidence (RWE) in regulatory submissions, their application to oncology drug approvals has seen limited success. Real-world data is most commonly summarized as a benchmark control for a single arm study or used to augment the concurrent control in a randomized clinical trial (RCT). While there has been substantial research on usage of RWD/RWE, our goal is to provide a comprehensive overview of their use in oncology drug approval submissions to inform future RWD/RWE study design. We will review examples of applications and summarize the strengths and weaknesses of each example identified by regulatory agencies. A few noteworthy case studies will be reviewed in detail. Operational aspects of RWD/RWE study design/analysis will be also discussed.

Family-wise type I error rate control for an extended 2-in-1 design with graphical approach in oncology drug development.

Nowadays, in oncology drug development, when an experimental treatment shows a promising anti-tumor effect in Phase I efficacy expansion, a Phase III pivotal trial may be launched directly. To mitigate the risk of skipping the traditional randomized Phase II proof of concept (POC) study, the 2-in-1 design was proposed by Chen et al. (2018). This design has gained great research and application interest since its publication and been extended in many ways. The original 2-in-1 design controls family-wise type I error rate (FWER) for one hypothesis in Phase II part and one hypothesis in Phase III part. However, in practice, for a stand-alone Phase III study usually there are multiple hypotheses with group sequential interim analyses and the multiplicity is controlled by the graphical approach. It is desirable that these features of the Phase III design are retained when 2-in-1 design is considered. The multiplicity control for a 2-in-1 design with multiple hypotheses in Phase III has been addressed mainly by the Bonferroni approach in the literature. For the more powerful graphical approach, while Jin and Zhang (2021) discussed the FWER control for a special 2-in-1 design, in which Phase II and Phase III have exactly the same hypotheses, the FWER control for a more common 2-in-1 design (i.e., one hypothesis in Phase II and multiple hypotheses in Phase III) is yet investigated. This paper provides the analytical conditions under which FWER is controlled with the graphical approach in such a 2-in-1 design. It also provides the numeric explorations of FWER control for such design with group sequential interim analyses in Phase III, as a direct Phase III design normally would have. As a result, our work helps lower the hurdle of the application of the 2-in-1 design and pave the way for its wider application.

Assessment of added activity of an antitumor agent.

An unprecedented number of novel oncology drugs are under preclinical and clinical development, and nearly all are developed in combinations. With an over-reliance on biological hypotheses, there is less effort to establish single agent activity before initiating late clinical development. This may be contributing to a decreased success rate going from phase 1 to approval in the immunotherapy era. Growing evidence in clinical trial data shows that the treatment benefit from most approved combination therapies can be explained by the independent drug action model. Using this working model, we develop a simple index to measure the added antitumor activity of a new drug based on mean response duration, an endpoint that naturally combines both response status and duration information for all patients, which is shown to be highly predictive of clinical benefit of FDA-approved anti-PD-(L)1 immunotherapies. This index sheds light on challenges and opportunities in contemporary oncology drug development and provides a practical tool to assist with decision-making in early clinical trials.

A novel framework of Bayesian optimal interval design for phase I trials with late-onset toxicities.

As molecularly targeted agents (MTAs) and immunotherapies have widely demonstrated delayed toxicity profile after multiple treatment cycles, the traditional phase I dose-finding designs may not be appropriate anymore because they just account for the acute toxicities occurring in the early period of treatment. When the dose-limiting toxicity (DLT) assessment window is prolonged to account for late-onset DLTs, it will cause logistic issues if the enrollment is suspended until all the DLT information is collected. We propose a novel framework to estimate the toxicity probability in the scenarios where some patients' DLT information are not complete and then implement the Bayesian optimal interval (BOIN) design to make decisions on dose escalation/de-escalation. Our proposed approach maintains BOIN's transparency by simply comparing the estimated toxicity probability with the escalation/de-escalation boundaries to decide the next dose level. The numerical studies show that our proposed framework can achieve comparable operating characteristics as other dose-finding designs considering late-onset DLTs, thus providing an attractive option of phase I dose-finding clinical trials for MTAs and immunotherapies.

Multiplicity for a group sequential trial with biomarker subpopulations.

Biomarker subpopulations have become increasingly important for drug development in targeted therapies. The use of biomarkers has the potential to facilitate more effective outcomes by guiding patient selection appropriately, thus enhancing the benefit-risk profile and improving trial power. Studying a broad population simultaneously with a more targeted one allows the trial to determine the population for which a treatment is effective and allows a goal of making approved regulatory labeling as inclusive as is appropriate. We examine new methods accounting for the complete correlation structure in group sequential designs with hypotheses in nested subgroups. The designs provide full control of family-wise Type I error rate. This extension of previous methods accounting for either group sequential design or correlation between subgroups improves efficiency (power or sample size) over a typical Bonferroni approach for testing nested populations.

Independent action models and prediction of combination treatment effects for response rate, duration of response and tumor size change in oncology drug development.

An unprecedented number of new cancer targets are in development, and most are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage development. The most common early endpoints to be assessed in such decision-making include objective response rate, duration of response and tumor size change. In this paper, using independent-drug-action and Bliss-drug-independence concepts as a foundation, we introduce simple models to predict combination therapy efficacy for duration of response and tumor size change. These models complement previous publications using the independent action models (Palmer 2017, Schmidt 2020) to predict progression-free survival and objective response rate and serve as new predictive models to understand drug combinations for early endpoints. The models can be applied to predict the combination treatment effect for early endpoints given monotherapy data, or to estimate the possible effect of one monotherapy in the combination if data are available from the combination therapy and the other monotherapy. Such quantitative work facilitates strategic planning and decision making in early stage oncology drug development.

Optimal two-stage randomized multinomial designs for Phase II oncology trials.

A new two-stage design is proposed that is suitable for early detection of the anticancer activity of experimental therapies in Phase II oncology trials. The endpoints of interest are response rate and early progression rate. The anticancer activity is defined by a positive signal in one endpoint and a non-negative signal in the other endpoint. The two endpoints are modeled by the multinomial distribution. The design is optimal in that it minimizes the patient exposure when the experimental therapies are inactive. The design parameters are found by a grid searching algorithm under type I and type II error rate constraints. Examples of the design are also presented in this paper.

Testing monotherapy and combination therapy in one trial with biomarker consideration.

It is a common scenario that an experimental oncology therapy, as a monotherapy, may be more effective than standard of care (SOC) in a biomarker positive population but less so or even inferior to SOC in biomarker negative population. At the same time, due to synergistic or additive effect, the combination of the two may be more effective than SOC alone in the all-comer population. The conventional development paradigm is to conduct two separate Phase III trials, one with the monotherapy versus SOC in the biomarker positive population, and the other with the combination therapy versus SOC in the all-comer population. In this manuscript, we propose a one-trial design that stratifies by biomarker status and randomizes biomarker positive patients into three arms (combination therapy, monotherapy, and SOC) and biomarker negative patients into two arms (combination therapy and SOC). There are two hypotheses in the proposed design and each addresses a different question. The family-wise type-I error rate (FWER) is smaller, due to shared control, than that of two separate trials. Therefore, no FWER adjustment is necessary in the proposed design and each hypothesis can be tested at the conventional 2.5% (one-sided) alpha level. The population for comparison between the combination therapy and SOC is skewed in the proposed design. A two-step log-rank statistic is proposed to account for the skewness. Power and sample size of the proposed design are evaluated in comparison with the two-trial paradigm. The proposed design is more efficient.

A study to determine the effects of food and multiple dosing on the pharmacokinetics of vorinostat given orally to patients with advanced cancer.

PURPOSE: This phase I study, conducted in advanced-stage cancer patients, assessed the safety and tolerability of oral vorinostat (suberoylanilide hydroxamic acid), single-dose and multiple-dose pharmacokinetics of vorinostat, and the effect of a high-fat meal on vorinostat pharmacokinetics. EXPERIMENTAL DESIGN: Patients (n = 23) received single doses of 400 mg vorinostat on day 1 (fasted) and day 5 (fed) with 48 hours of pharmacokinetic sampling on both days. Patients received 400 mg vorinostat once daily on days 7 to 28. On day 28, vorinostat was given (fed) with pharmacokinetic sampling for 24 hours after dose. RESULTS: The apparent t(1/2) of vorinostat was short (approximately 1.5 hours). A high-fat meal was associated with a small increase in the extent of absorption and a modest decrease in the rate of absorption. A short lag time was observed before detectable levels of vorinostat were observed in the fed state, and T(max) was delayed. Vorinostat concentrations were qualitatively similar following single-dose and multiple-dose administration; the accumulation ratio based on area under the curve was 1.21. The elimination of vorinostat occurred primarily through metabolism, with <1% of the given dose recovered intact in urine. The most common vorinostat-related adverse experiences were mild to moderate nausea, anorexia, fatigue, increased blood creatinine, and vomiting. CONCLUSIONS: Vorinostat concentrations were qualitatively similar after single and multiple doses. A high-fat meal increased the extent and modestly decreased the rate of absorption of vorinostat; this effect is not anticipated to be clinically meaningful. Continued investigation of 400 mg vorinostat given once daily in phase II and III efficacy studies is warranted.