A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling.

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.

Flexible seamless 2-in-1 design with sample size adaptation.

The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

DOD-Combo: Bayesian dose finding design in combination trials with meta-analytic-predictive prior.

Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method's superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials.

BEATS: Bayesian hybrid design with flexible sample size adaptation for time-to-event endpoints.

As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta-analytic predictive prior (BaSe-MAP) to incorporate time-to-event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.

Intrusive social support among Black and White individuals with type 2 diabetes: A "Control issue" or a sign of "Concern and love"?

Family members and friends play an important supportive role in the management of chronic illnesses like diabetes, which often require substantial lifestyle changes. Some studies suggest that there may be racial differences in the kinds of support people receive, though little research has examined this idea within a chronic illness context. The current research takes a qualitative approach to examining similarities and differences between Black and White individuals with type 2 diabetes in the dimensions of support received from their family members, with a particular focus on better understanding more intrusive forms of support, such as unsolicited and overprotective support. Semi-structured interviews were conducted (N = 32) to characterize differences in support received by Black and White individuals with type 2 diabetes. The results of the thematic analysis suggested that unsolicited and overprotective support were not universally perceived to be negative, as previous work on White populations seemed to suggest. Rather, if the support provided was perceived as inhibiting autonomy, it was generally undesired by participants from both racial groups-however, for Black participants, knowing that the support was provided out of love could make it more acceptable. The analysis also revealed several underexplored dimensions of received support, including the directiveness of support and the tone used to deliver support. The current study provides an initial step towards grounding social support theory in the experiences of marginalized populations and will inform further development of a culturally sensitive measure of social support for individuals with chronic illness.

Incorporating historical information to improve dose optimization design with toxicity and efficacy endpoints: iBOIN-ET.

In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno-oncology and cell therapy, the complex dose-toxicity and dose-efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN-ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN-ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process.

PMED: Optimal Bayesian Platform Trial Design with Multiple Endpoints.

In oncology drug development, indication selection and optimal dose identification are the primary objectives for the early phase of clinical trials and could significantly impact the probability of success. Master protocols, e.g., basket trial, umbrella trial, and platform trial, have become popular in practice considering the connection of trial designs with multiple indications and treatment candidates. They also enable the optimization of operational resources and maximize the capability of data-driven decision-making. However, most of the available designs are developed with the efficacy endpoint only for treatment effect estimation and testing, without consideration of the safety end point. Thus, it often lacks a comprehensive quantitative framework to allow optimal treatment selection, which could put future development at risk. We propose an optimal Bayesian platform trial design with multiple end points (PMED) to characterize the overall benefit-risk profile. The design is further extended to allow treatment and indication selection within and across arms, with continuous monitoring on multiple interim analyses for futility. In addition, we propose dynamic borrowing across arms to increase the efficiency and accuracy of estimation given the level of similarity across arms. A hierarchical hypothesis structure is utilized to achieve optimal indication and treatment combination selection by controlling family-wise error. Through simulation studies, we show that PMED is a robust design under the studied scenarios with superb power and controlled family-wise error rate.

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.

Older patients (aged ≥60 years) with previously untreated advanced-stage classical Hodgkin lymphoma: a detailed analysis from the phase III ECHELON-1 study.

Effective and tolerable treatments are needed for older patients with classical Hodgkin lymphoma. We report results for older patients with classical Hodgkin lymphoma treated in the large phase III ECHELON-1 study of frontline brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine (A+AVD) versus doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD). Modified progression-free survival per independent review facility for older versus younger patients (aged ≥60 vs. <60 years) was a pre-specified subgroup analysis; as the ECHELON- 1 study was not powered for these analyses, reported P-values are descriptive. Of 1,334 enrolled patients, 186 (14%) were aged ≥60 years (A+AVD: n=84, ABVD: n=102); results below refer to this age group. Modified progression-free survival per independent review facility was similar in the two arms at 24 months (A+AVD: 70.3% [95% confidence interval (CI): 58.4-79.4], ABVD: 71.4% [95% CI: 60.5-79.8], hazard ratio (HR)=1.00 [95% CI: 0.58-1.72], P=0.993). After a median follow-up of 60.9 months, 5-year progression-free survival per investigator was 67.1% with A+AVD versus 61.6% with ABVD (HR=0.820 [95% CI: 0.494-1.362], P=0.443). Comparing A+AVD versus ABVD, grade 3/4 peripheral neuropathy occurred in 18% versus 3%; any-grade febrile neutropenia in 37% versus 17%; and any-grade pulmonary toxicity in 2% versus 13%, respectively, with three (3%) pulmonary toxicity-related deaths in patients receiving ABVD (none in those receiving A+AVD). Altogether, A+AVD showed overall similar efficacy to ABVD with survival rates in both arms comparing favorably to those of prior series in older patients with advanced-stage classical Hodgkin lymphoma. Compared to ABVD, A+AVD was associated with higher rates of neuropathy and neutropenia, but lower rates of pulmonary-related toxicity. Trials registered at ClinicalTrials.gov identifiers: NCT01712490; EudraCT number: 2011-005450-60.

Brentuximab vedotin with chemotherapy for stage III or IV classical Hodgkin lymphoma (ECHELON-1): 5-year update of an international, open-label, randomised, phase 3 trial.

BACKGROUND: Despite advances in the treatment of Hodgkin lymphoma with the introduction of PET-adapted regimens, practical challenges prevent more widespread use of these approaches. The ECHELON-1 study assessed the safety and efficacy of front-line A+AVD (brentuximab vedotin, doxorubicin, vinblastine, and dacarbazine) versus ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) in patients with stage III or IV classical Hodgkin lymphoma. The primary analysis showed improved modified progression-free survival with A+AVD. We present an updated analysis of ECHELON-1 at 5 years, an important landmark for this patient population. METHODS: ECHELON-1 was an international, open-label, randomised, phase 3 trial done at 218 clinical sites, including hospitals, cancer centres, and community clinics, in 21 countries. Previously untreated patients (≥18 years with an Eastern Cooperative Oncology Group performance status of ≤2) with stage III or IV classical Hodgkin lymphoma were randomly assigned (1:1) to receive A+AVD (brentuximab vedotin, 1·2 mg/kg of bodyweight, doxorubicin 25 mg/m2 of body surface area, vinblastine 6 mg/m2, and dacarbazine 375 mg/m2) or ABVD (doxorubicin 25 mg/m2, bleomycin 10 U/m2, vinblastine 6 mg/m2, and dacarbazine 375 mg/m2) intravenously on days 1 and 15 of each 28-day cycle for up to six cycles. Stratification factors included region (Americas vs Europe vs Asia) and International Prognostic Score risk group (low, intermediate, or high risk). The primary endpoint was modified progression-free survival; this 5-year update includes analysis of progression-free survival as per investigator assessment in the intention-to-treat population, which was an exploratory endpoint, although the 5-year analysis was not prespecified in the protocol. This trial is registered with ClinicalTrials.gov (NCT01712490) and EudraCT (2011-005450-60), and is ongoing. FINDINGS: Between Nov 19, 2012, and Jan 13, 2016, 1334 patients were randomly assigned to receive A+AVD (n=664) or ABVD (n=670). At a median follow-up of 60·9 months (IQR 52·2-67·3), 5-year progression-free survival was 82·2% (95% CI 79·0-85·0) with A+AVD and 75·3% (71·7-78·5) with ABVD (hazard ratio [HR] 0·68 [95% CI 0·53-0·87]; p=0·0017). Among PET-2-negative patients, 5-year progression-free survival was higher with A+AVD than with ABVD (84·9% [95% CI 81·7-87·6] vs 78·9% [75·2-82·1]; HR 0·66 [95% CI 0·50-0·88]; p=0·0035). 5-year progression-free survival for PET-2-positive patients was 60·6% (95% CI 45·0-73·1) with A+AVD versus 45·9% (32·7-58·2) with ABVD (HR 0·70 [95% CI 0·39-1·26]; p=0·23). Peripheral neuropathy continued to improve or resolve over time with both A+AVD (375 [85%] of 443 patients) and ABVD (245 [86%] of 286 patients); more patients had ongoing peripheral neuropathy in the A+AVD group (127 [19%] of 662) than in the ABVD group (59 [9%] of 659). Fewer secondary malignancies were reported with A+AVD (19 [3%] of 662) than with ABVD (29 [4%] of 659). More livebirths were reported in the A+AVD group (n=75) than in the ABVD group (n=50). INTERPRETATION: With 5 years of follow-up, A+AVD showed robust and durable improvement in progression-free survival versus ABVD, regardless of PET-2 status, and a consistent safety profile. On the basis of these findings, A+AVD should be preferred over ABVD for patients with previously untreated stage III or IV classical Hodgkin lymphoma. FUNDING: Millennium Pharmaceuticals (a wholly owned subsidiary of Takeda Pharmaceutical Company), and Seagen.

Brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine in patients with advanced-stage, classical Hodgkin lymphoma: A prespecified subgroup analysis of high-risk patients from the ECHELON-1 study.

Approximately one-third of patients diagnosed with Hodgkin lymphoma presenting with Stage IV disease do not survive past 5 years. We present updated efficacy and safety analyses in high-risk patient subgroups, defined by Stage IV disease or International Prognostic Score (IPS) of 4-7, enrolled in the ECHELON-1 study that compared brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine (A + AVD) versus doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) as first-line therapy after a median follow-up of 37.1 months. Among patients treated with A + AVD (n = 664) or ABVD (n = 670), 64% had Stage IV disease and 26% had an IPS of 4-7. Patients with Stage IV disease treated with A + AVD showed consistent improvements in PFS at 3 years as assessed by investigator (hazard ratio [HR], 0.723; 95% confidence interval [CI], 0.537-0.973; p = 0.032). Similar improvements were seen in the subgroup of patients with IPS of 4-7 (HR, 0.588; 95% CI, 0.386-0.894; p = 0.012). The most common adverse events (AEs) in A + AVD-treated versus ABVD-treated patients with Stage IV disease were peripheral neuropathy (67% vs. 40%) and neutropenia (71% vs. 55%); in patients with IPS of 4-7, the most common AEs were peripheral neuropathy (69% vs. 45%), neutropenia (66% vs. 55%), and febrile neutropenia (23% vs. 9%), respectively. Patients in high-risk subgroups did not experience greater AE incidence or severity than patients in the total population. This updated analysis of ECHELON-1 shows a favorable benefit-risk balance in high-risk patients.

TEPI-2 and UBI: designs for optimal immuno-oncology and cell therapy dose finding with toxicity and efficacy.

Conventional dose finding designs in oncology drug development target on the identification of the maximum tolerated dose (MTD), with the assumption that the MTD has the most potential of clinical activity among those identified tolerable dose levels. However, immuno-oncology (I-O) and cell therapy area, may lack dose-efficacy monotonicity, posing significant challenges in the statistical designs for dose finding trials. A desirable design should empower the trial to identify the right dose level with tolerable toxicity and acceptable efficacy. Such dose is called as optimal biological dose (OBD), which is more appropriate to be considered as the primary objective of the first-in-human trial in I-O and cell therapy than MTD. We propose two model-assisted designs in this setting: the toxicity and efficacy probability interval-2 (TEPI-2) design and the utility-based interval (UBI) design that incorporate the toxicity and efficacy outcomes simultaneously and identify a dose that has high probability of acceptable efficacy with manageable toxicity. The proposed designs can generate decision tables before trial starts to facilitate practical and easy-to-implement applications. Through simulation studies, our proposed novel designs demonstrate superior performance in accuracy, efficiency, and safety. Additionally, they can reduce the number of patients and shorten clinical development timeline. We also illustrate the advantages of proposed methods by redesigning a CAR T-cell therapy phase I clinical trial for multiple myeloma and summarize our recommendations in the discussion section.

Flexible Bayesian subgroup analysis in early and confirmatory trials.

Subgroup analysis is one of the most important issues in clinical trials. In confirmatory trials, it is critical to investigate consistency of the treatment effect across subgroups, which could potentially result in incorrect scientific conclusion or regulatory decision. There are many challenges and methodological complications of interpreting subgroup results beyond the regulatory setting. For the early phase or proof of concept trials, particularly in basket trials, it is also important to have reliable estimation of subgroup treatment effect in order to guide the next phase go/no-go decision making when large biases can be introduced due to small sample size or random variability. In this paper, we review several recent methods that have been proposed for subgroup analysis in the Bayesian framework to correct for bias. We present simulation results from applying various novel Bayesian hierarchical models for subgroup analysis to a phase II basket trial. For different scenarios considered, we compare the average total sample size, and frequentist-like operating characteristics of power and familywise type I error rate. We compare the precision of the model estimates of the treatment effect by assessing average relative bias and the width of the 95% credible interval for the bias. We also demonstrate flexible Bayesian hierarchical models in a case study of a phase III oncology trial for subgroup treatment effect estimation to help with regulatory decision making. Finally, we conclude our findings in the discussion section and give recommendations on how these methods could be implemented in confirmatory and early phase clinical trials.

Primary prophylaxis with G-CSF may improve outcomes in patients with newly diagnosed stage III/IV Hodgkin lymphoma treated with brentuximab vedotin plus chemotherapy.

We investigate the impact of granulocyte-colony stimulating factor (G-CSF) primary prophylaxis (G-PP, N = 83) versus no G-PP (N = 579) on safety and efficacy of brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine (A + AVD) in the ECHELON-1 study of previously untreated stage III/IV classical Hodgkin lymphoma. G-PP was associated with lower incidence of ≥ grade 3 neutropenia (29% versus 70%) and febrile neutropenia (11% versus 21%). Fewer dose delays (35% versus 49%), reductions (20% versus 26%), and hospitalizations (29% versus 38%) were observed. Seven neutropenia-associated deaths occurred in the A + AVD arm; none received G-PP. A + AVD with G-PP was associated with decreased risk of a modified progression-free survival event by 26% compared with A + AVD alone (95% CI: 0.40-1.37). G-PP reduced the rate and severity of adverse events, including febrile neutropenia, reduced treatment delays, dose reductions, and discontinuations, and may thus improve efficacy outcomes. These data support G-PP for all patients treated with A + AVD.

Design considerations for phase I/II dose finding clinical trials in Immuno-oncology and cell therapy.

Immuno-oncology (IO) and cell therapy, the frontier of cancer treatment, is a rapidly developing area that brings new opportunities to patients. In IO and cell therapy clinical trial development, it is critical to identify the right dose level in early phase of trials thus improving the probability of success in confirmatory trials to test the superiority over other therapies. Given the complex mechanism interacting with immune system for IO drugs especially cell therapy, the traditional oncology dose finding trial designs may not serve the purpose. Specifically, it is questionable to believe the monotone relationship between dose level and safety/efficacy, which will likely result in inappropriate dose selection using designs with monotone assumption. Additionally, considering the immune system pathway, designs ignoring the heterogeneity of the patient populations may provide misleading dose decisions, which could be either unsafe or lead to selection mistakes for targeted population. Therefore, in our paper, we review and present the limitations of the traditional dose finding designs. Then we discuss improved dose finding designs that consider both safety and efficacy outcomes simultaneously. Furthermore, we propose novel dose finding designs for multiple populations: BNP-mTPI and fBNP-mTPI, which extend the modified toxicity probability interval designs by utilizing Bayesian non-parametric priors. The proposed designs consider patient population differences meanwhile flexibly borrowing information across populations with similar profiles to improve the efficiency of dose search and accuracy of estimation of optimal dose level. Simulations are provided to demonstrate the model performance. Finally, we conclude the recommendations for IO and cell therapy dose finding designs in the discussion and offer insights for future research direction.

Bayesian Semi-parametric Design (BSD) for adaptive dose-finding with multiple strata.

In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability-non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.

Brentuximab vedotin with chemotherapy for stage III/IV classical Hodgkin lymphoma: 3-year update of the ECHELON-1 study.

The phase 3 ECHELON-1 study demonstrated that brentuximab vedotin (A) with doxorubicin, vinblastine, and dacarbazine (AVD; A+AVD) exhibited superior modified progression-free survival (PFS) vs doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) for frontline treatment of patients with stage III/IV classical Hodgkin lymphoma (cHL). Maturing positron emission tomography (PET)-adapted trial data highlight potential limitations of PET-adapted approaches, including toxicities with dose intensification and higher-than-expected relapse rates in PET scan after cycle 2 (PET2)-negative (PET2-) patients. We present an update of the ECHELON-1 study, including an exploratory analysis of 3-year PFS per investigator. A total of 1334 patients with stage III or IV cHL were randomized 1:1 to receive 6 cycles of A+AVD (n = 664) or ABVD (n = 670). Interim PET2 was required. At median follow-up of 37 months, 3-year PFS rates were 83.1% with A+AVD and 76.0% with ABVD; 3-year PFS rates in PET2- patients aged <60 years were 87.2% vs 81.0%, respectively. A beneficial trend in PET2+ patients aged <60 years on A+AVD was also observed, with a 3-year PFS rate of 69.2% vs 54.7% with ABVD. The benefit of A+AVD in the intent-to-treat population appeared independent of disease stage and prognostic risk factors. Upon continued follow-up, 78% of patients with peripheral neuropathy on A+AVD had either complete resolution or improvement compared with 83% on ABVD. These data highlight that A+AVD provides a durable efficacy benefit compared with ABVD for frontline stage III/IV cHL, consistent across key subgroups regardless of patient status at PET2, without need for treatment intensification or bleomycin exposure. This trial was registered at www.clinicaltrials.gov as #NCT01712490 (EudraCT no. 2011-005450-60).

No meaningful drug interactions with doravirine, lamivudine and tenofovir disoproxil fumarate coadministration.

BACKGROUND: Doravirine (DOR) is a novel non-nucleoside reverse transcriptase inhibitor available as a single tablet and a three-drug combination with lamivudine (3TC) and tenofovir disoproxil fumarate (TDF) to treat HIV-1 infection. These analyses assessed pharmacokinetic (PK) interactions with coadministration. METHODS: Two trials were conducted. Study 1: two-period, fixed-sequence; eight healthy participants; Period 1, DOR 100 mg followed by ≥7-day washout; Period 2, TDF 300 mg once daily for 18 days, coadministration of DOR 100 mg on day 14. Study 2: three-period, crossover, 15 healthy participants; Treatment A, DOR 100 mg; Treatment B, 3TC 300 mg + TDF 300 mg; Treatment C, DOR 100 mg + 3TC 300 mg + TDF 300 mg; ≥7-day washout between periods. RESULTS: Study 1: geometric mean ratios (GMRs; 90% confidence interval [CI]) of DOR area under the concentration-time curve from time 0 extrapolated to infinity (AUC0-∞) and observed plasma concentrations at 24 h post-dose (C24 h; DOR+TDF/DOR) were 0.95 (0.80, 1.12) and 0.94 (0.78, 1.12), respectively. Study 2: GMRs (90% CI) of DOR AUC0-∞ and C24 h (DOR+3TC+TDF/DOR) were 0.96 (0.87, 1.06) and 0.94 (0.83, 1.06), respectively. GMRs (90% CI) of 3TC and tenofovir AUC0-∞ (DOR+3TC+TDF/3TC+TDF) were 0.94 (0.88, 1.00) and 1.11 (0.97, 1.28), respectively. Study drugs were generally well tolerated. CONCLUSIONS: Multiple doses of TDF did not have a clinically meaningful effect on DOR PK. The PK of DOR were similar when administered alone or in combination with 3TC and TDF. DOR had no meaningful effect on the PK of 3TC and tenofovir.

Characterisation of the absorption, distribution, metabolism, excretion and mass balance of doravirine, a non-nucleoside reverse transcriptase inhibitor in humans.

Absorption, distribution, metabolism and elimination of doravirine (MK-1439), a novel non-nucleoside reverse transcriptase inhibitor, were investigated. Two clinical trials were conducted in healthy subjects: an oral single dose [14 C]doravirine (350 mg, ∼200 µCi) trial (n = 6) and an intravenous (IV) single-dose doravirine (100 µg) trial (n = 12). In vitro metabolism, protein binding, apparent permeability and P-glycoprotein (P-gp) transport studies were conducted to complement the clinical trials. Following oral [14 C]doravirine administration, all of the administered dose was recovered. The absorbed dose was eliminated primarily via metabolism. An oxidative metabolite (M9) was the predominant metabolite in excreta and was the primary circulating metabolite (12.9% of circulating radioactivity). Following IV administration, doravirine clearance and volume of distribution were 3.73 L/h (95% confidence intervals (CI) 3.09, 4.49) and 60.5 L (95% CI 53.7, 68.4), respectively. In vitro, doravirine is not highly bound to plasma proteins (unbound fraction 0.24) and has good passive permeability. The metabolite M9 was generated by cytochrome P450 3A (CYP3A)4/5-mediated oxidation. Doravirine was a P-gp substrate but P-gp efflux is not expected to play a significant role in limiting doravirine absorption or to be involved in the elimination of doravirine. In conclusion, doravirine is a low clearance drug, primarily eliminated by CYP3A-mediated metabolism.