Efficacy-toxicity Trade-off Research Articles

Phase I/II Design for Selecting Subgroup-Specific Optimal Biological Doses for Prespecified Subgroups.

We propose a phase I/II trial design to support dose-finding when the optimal biological dose (OBD) may differ in two prespecified patient subgroups. The proposed design uses a utility function to quantify efficacy-toxicity trade-offs, and a Bayesian model with spike and slab prior distributions for the subgroup effect on toxicity and efficacy to guide dosing and to facilitate identifying either subgroup-specific OBDs or a common OBD depending on the resulting trial data. In a simulation study, we find the proposed design performs nearly as well as a design that ignores subgroups when the dose-toxicity and dose-efficacy relationships are the same in both subgroups, and nearly as well as a design with independent dose-finding within each subgroup when these relationships differ across subgroups. In other words, the proposed adaptive design performs similarly to the design that would be chosen if investigators possessed foreknowledge about whether the dose-toxicity and/or dose-efficacy relationship differs across two prespecified subgroups. Thus, the proposed design may be effective for OBD selection when uncertainty exists about whether the OBD differs in two prespecified subgroups.

The patient perspective on dose optimization for anticancer treatments: A new era of cancer drug dosing-Challenging the "more is better" dogma.

The Patient-Centered Dosing Initiative, a patient-led effort advocating for a paradigm shift in determining cancer drug dosing strategies, pioneers a departure from traditional oncology drug dosing practices. Historically, oncology drug dosing relies on identifying the maximum tolerated dose through phase 1 dose escalation methodology, favoring higher dosing for greater efficacy, often leading to higher toxicity. However, this approach is not universally applicable, especially for newer treatments like targeted therapies and immunotherapies. Patient-Centered Dosing Initiative challenges this "more is better" ethos, particularly as metastatic breast cancer patients themselves, as they not only seek longevity but also a high quality of life since most metastatic breast cancer patients stay on treatment for the rest of their lives. Surveying 1221 metastatic breast cancer patients and 119 oncologists revealed an evident need for flexible dosing strategies, advocating personalized care discussions based on patient attributes. The survey results also demonstrated an openness toward flexible dosing and a willingness from both patients and clinicians to discuss dosing as part of their care. Patient-centered dosing emphasizes dialogue between clinicians and patients, delving into treatment efficacy-toxicity trade-offs. Similarly, clinical trial advocacy for multiple dosing regimens encourages adaptive strategies, moving away from strict adherence to maximum tolerated dose, supported by recent research in optimizing drug dosages. Recognizing the efficacy-effectiveness gap between clinical trials and real-world practice, Patient-Centered Dosing Initiative underscores the necessity for patient-centered dosing strategies. A focus on individual patient attributes aligns with initiatives like Project Optimus and Project Renewal, aiming to optimize drug dosages for improved treatment outcomes at both the pre- and post-approval phases. Patient-Centered Dosing Initiative's efforts extend to patient education, providing tools to initiate dosage-related conversations with physicians. In addition, it emphasizes physician-patient dialogues and post-marketing studies as essential in determining optimal dosing and refining drug regimens. A dose-finding paradigm prioritizing drug safety, tolerability, and efficacy benefits all stakeholders, reducing emergency care needs and missed treatments for patients, aligning with oncologists' and patients' shared goals. Importantly, it represents a win-win scenario across healthcare sectors. In summary, the Patient-Centered Dosing Initiative drives transformative changes in cancer drug dosing, emphasizing patient well-being and personalized care, aiming to enhance treatment outcomes and optimize oncology drug delivery.

Phase Ib adaptive study of dasatinib for the prevention of oxaliplatin-induced neuropathy in patients with gastrointestinal (GI) cancers receiving FOLFOX chemotherapy with or without bevacizumab.

745 Background: Neurotoxicity is one of the most significant and disabling side effects of oxaliplatin (OX) and frequently limits the amount of OX that can be used. OX uptake by organic cation transporter 2 (OCT2) into mouse and rat dorsal root ganglia is a prerequisite for OX-induced peripheral neuropathy (OIPN). Preclinical data in rat and mouse models from our group showed that by inhibiting YES1, pre-treatment with dasatinib (D) inactivated OCT2 and prevented acute and chronic OIPN. A phase I trial is ongoing investigating addition of D prior to OX to prevent OIPN. Methods: This was a Bayesian Phase 1b dose-finding study with adaptive dose selection using efficacy-toxicity trade-offs in patients (pts) with GI cancers who are candidates for mFOLFOX6 ± bevacizumab [NCT04164069]. Pts received chemotherapy on day 1 and 15 schedule, every 28 days. D was administered at one of 2 dose levels – 100mg or 140mg PO, given 24 hours and 30 mins prior to OX on C1D14, C1D15 respectively and repeated on C1D28 and C2D1. During cycle 1, pharmacokinetic and pharmacodynamic data was obtained at baseline, during, and up to 24 hours after D or OX administration. Recommended Phase 2 dose (RP2D) was defined as the lowest intermittent dose of D that affects serum biomarkers of OCT2, including methylnicotinamide (NMN) and creatinine, by ≥2-fold without influencing the clearance of oxaliplatin by ≥20%. CIPN20 questionnaire was completed prior to each dose of FOLFOX on screening through to C3D1. Weakly informative Jeffreys priors were used for response and toxicity probabilities, with the latter constrained to be increasing with dose. Results: 13 pts were screened. 5 pts enrolled in cohort 1 (D 100 mg), 2 in cohort 2 (D 140 mg) and 2 in cohort 3 (D 140mg). 6 pts were not evaluable for the efficacy and toxicity endpoints due to screen failure or early study discontinuation. 7 patients were evaluated for efficacy biomarkers and 6 pts were evaluated for both efficacy biomarkers and toxicity. There were no DLTs observed in any cohort. 4/7 pts had ≥2-fold increase in AUC of NMN, and 3/7 pts had ≥2-fold increase in AUC of creatinine. 6/7 pts had ≤20% change in the clearance of OX. Conclusions: Addition of D 100mg or 140mg to traditional FOLFOX regimen was safe and in the majority of pts appeared to alter serum biomarkers of OCT2 in a manner suggesting inhibition of OX uptake in the DRG. 140mg was the chosen RP2D. A randomized phase II study is planned using D to prevent OPIN. Clinical trial information: NCT04164069 . [Table: see text]

How large must a dose-optimization trial be?

Recently promulgated Draft Guidance from the US Food and Drug Administration Oncology Center of Excellence (OCE) recommends randomized, parallel dose-response trials for "dose optimization," but with vaguely stated aims that engage none of the statistical principles which typically attend randomization. Here, I advance a criterion for reasonable precision of such trials, and examine its implications for minimum enrollment, within a utility-based framework that acknowledges interindividual heterogeneity simultaneously in pharmacokinetics/pharmacodynamics and in the subjective evaluation of efficacy-toxicity trade-offs. Even when designed and conducted under ideal circumstances, reasonably sized trials of the kind advocated by OCE may need to enroll many hundreds of participants.

A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory

Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the ‘indirect’ effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or ‘indirect’ effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.

A phase Ib adaptive study of dasatinib for the prevention of oxaliplatin-induced neuropathy in patients with gastrointestinal (GI) cancers receiving FOLFOX chemotherapy with or without bevacizumab.

e15613 Background: Neurotoxicity is one of the most significant and disabling side effects of oxaliplatin (OX) and frequently limits the amount of OX that can be used. OX uptake by organic cation transporter 2 (OCT2) into mouse and rat dorsal root ganglia is a prerequisite for OX-induced peripheral neuropathy (OIPN). Preclinical data in rat and mouse models from our group showed that by inhibiting YES1, pre-treatment with dasatinib (D) inactivated OCT2 and prevented acute and chronic OIPN. A phase I trial is ongoing investigating addition of D prior to OX to prevent OIPN. Methods: This is a Bayesian Phase 1b dose-finding study with adaptive dose selection using efficacy-toxicity trade-offs in patients (pts) with GI cancers who are candidates for mFOLFOX6 ± bevacizumab [NCT04164069]. Pts received chemotherapy on day 1 and 15 schedule, every 28 days. D was administered at one of 2 dose levels – 100mg or 140mg PO, given 24 hours and 30 mins prior to OX on C1D14, C1D15 respectively and repeated on C1D28 and C2D1. During cycle 1, pharmacokinetic and pharmacodynamic data was obtained at baseline, during, and up to 24 hours after D or OX administration. Recommended Phase 2 dose (RP2D) was defined as the lowest intermittent dose of D that increases serum biomarkers of OCT2, including methylnicotinamide (NMN) and creatinine, by ≥2-fold without influencing the clearance of oxaliplatin by ≥20%, toxicity profile was evaluated based on CTCAE v. 5.0. Results: 11 pts were screened. 5 pts enrolled in cohort 1 (D 100 mg) and 2 in cohort 2 (D 140 mg). In this preliminary analysis, 5 patients were evaluated for efficacy biomarkers and 4 pts were evaluated for both efficacy biomarkers and toxicity. There were no DLTs observed in either cohort. 3/5 pts had ≥2-fold increase in AUC of NMN, and 4/5 pts had ≥2-fold increase in AUC of creatinine. 4/5 pts had ≤20% change in the clearance of OX. Conclusions: Addition of D 140 mg to traditional FOLFOX regimen was safe and resulted in increasing levels of biomarkers as indication of inhibition of OCT2 in comparison to baseline for the majority of pts enrolled. Enrollment and analysis are ongoing at D 140mg dose to confirm the RP2D to prevent OPIN in a future randomized phase II study. Clinical trial information: NCT04164069 . [Table: see text]

Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer.

Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial. The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution. Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation. Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.

MMAE Delivery Using the Bicycle Toxin Conjugate BT5528.

The EphA2 receptor is found at high levels in tumors and low levels in normal tissue and high EphA2 expression in biopsies is a predictor of poor outcome in patients. Drug discovery groups have therefore sought to develop EphA2-based therapies using small molecule, peptide, and nanoparticle-based approaches (1-3). However, until now only EphA2-targeting antibody-drug conjugates (ADC) have entered clinical development. For example, MEDI-547 is an EphA2-targeting ADC that displayed encouraging antitumor activity in preclinical models and progressed to phase I clinical testing in man. Here we describe the development of BT5528, a bicyclic peptide ("Bicycle") conjugated to the auristatin derivative maleimidocaproyl-monomethyl auristatin E to generate the Bicycle toxin conjugate BT5528. The report compares and contrasts the Pharmacokinetics (PK) characteristics of antibody and Bicycle-based targeting systems and discusses how the PK and payload characteristics of different delivery systems impact the efficacy-toxicity trade off which is key to the development of successful cancer therapies. We show that BT5528 gives rise to rapid update into tumors and fast renal elimination followed by persistent toxin levels in tumors without prolonged exposure of parent drug in the vasculature. This fast in, fast out kinetics gave rise to more favorable toxicology findings in rats and monkeys than were observed with MEDI-547 in preclinical and clinical studies.Graphical Abstract: http://mct.aacrjournals.org/content/molcanther/19/7/1385/F1.large.jpg.

A phase Ib adaptive study of dasatinib for the prevention of oxaliplatin-induced neuropathy in patients with metastatic colorectal cancer receiving FOLFOX chemotherapy and bevacizumab.

TPS12125 Background: Neurotoxicity is one of the most significant and disabling side effects of oxaliplatin and frequently limits the cumulative amount that can be used. The mechanism of oxaliplatin-induced neurotoxicity remains uncertain, although our preliminary studies suggest that oxaliplatin uptake by organic cation transporter 2 (OCT2) into mouse and rat dorsal root ganglion is a prerequisite for oxaliplatin-induced peripheral neuropathy. The activity of OCT2 is dependent on tyrosine phosphorylation by the SRC-kinase family member Yes1, which is highly sensitive to inhibition by several FDA-approved, small molecule kinase inhibitors such as dasatinib. We have previously shown that pre-treatment with oral dasatinib prevented acute and chronic oxaliplatin-induced peripheral neuropathy in mouse and rat models. Methods: This is a phase Ib dose-finding study of dasatinib given in combination with mFOLFOX6 with or without bevacizumab. The study explores the hypothesis that the addition of dasatinib prior to oxaliplatin will inhibit OCT2 activity and reduce oxaliplatin-induced neuropathy. This hypothesis will be tested in a Bayesian Phase 1b trial with adaptive dose selection using efficacy-toxicity trade-offs (modified toxicity-efficacy probability interval dose-finding design) in patients with confirmed stage IV colorectal cancer who are candidates for mFOLFOX6 with bevacizumab therapy. Patients who have documented peripheral neuropathy or prior exposure to oxaliplatin will be excluded. The primary objective is to determine the recommended Phase 2 dose which is defined as the lowest intermittent dose of dasatinib that affects serum biomarkers of OCT2, including methylnicotinamide and creatinine, by ≥2-fold without influencing the clearance of oxaliplatin by > 20%. The following doses will be used: oxaliplatin 85mg/m2 IV, 5FU bolus 400mg/m2 IV bolus with Leucovorin 400mg/m2, bevacizumab 5mg/kg, followed by infusional 5FU 2400mg/m2 IV over 46 hours given on a day 1 and 15 schedule every 28 days. Dasatinib will be administered at one of 2 dose levels – 100mg or 140mg po. Dasatinib will be given 24 hours and 30 mins prior to oxaliplatin on C1D14, C1D15 respectively and repeated on C1D28 and C2D1. Secondary objectives include evaluation of the influence of dasatinib on the pharmacokinetics of oxaliplatin and vice versa. Quality of life will be explored using the CIPN20 questionnaire. The trial opened to enrollment in Dec 2019 (NCT04164069) and is accepting patients. Clinical trial information: NCT04164069 .

A phase Ib study of the safety and pharmacology of nilotinib to prevent paclitaxel-induced peripheral neuropathy in patients with breast cancer.

TPS12128 Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating adverse effect of paclitaxel, but convincing evidence for a preventative technique founded on mechanistic rationale is lacking. We previously report that paclitaxel accumulation is mediated by the murine organic anion transporting polypeptide, OATP1B2 in mice (OATP1B1 and OATP1B3 in humans) (Leblanc, J Clin Invest, 2018). We found that paclitaxel induces acute and chronic neurotoxicity in mice in an OATP1B2-dependent manner, which is reversed by pre-treatment with FDA-approved tyrosine kinase inhibitor, nilotinib. Methods: This phase Ib dose finding study of nilotinib in combination with weekly paclitaxel is investigating the hypothesis that intermittent dosing of nilotinib 24 hours before paclitaxel infusion (excluding C1D1) and again 30 minutes before paclitaxel infusion will inhibit OATP1B1 and prevent CIPN. This hypothesis will be assessed using an adaptive Bayesian method for dose finding based on efficacy-toxicity trade-offs in patients with breast cancer stages I-III who qualify for paclitaxel therapy. Patients with previous ≥ grade 2 neuropathy on breast cancer therapies will be excluded. The primary objectives are to find the recommended phase II dose of nilotinib in combination with paclitaxel for early stage breast cancer, defined as the lowest intermittent dose of nilotinib that temporarily inhibits OATP1B1 function without affecting paclitaxel plasma pharmacokinetics (PK), and to determine the toxicity profile of nilotinib in combination with paclitaxel. OATP1B1 inhibition by nilotinib will be assessed via validated surrogate endogenous substrates, glycochenodeoxycholate sulfate (GCDGA-S) and chenodeoxycholate-24-glucuronide (CDCA-24G). Effective OATP1B1 inhibition will be ≥5-fold increase in AUC of GCDCA-S from pre- to post- treatment or detectable CDCA-24G levels post-treatment. The 4 nilotinib dose levels are 50 mg (dose level -1), 100 mg (dose level 1), 200 mg (dose level 2), 300 mg (dose level 3). IV paclitaxel dose of 80 mg/m2 on D1,8,15 for 12 total weekly doses will be used. Oral nilotinib will be administered 24 hours before paclitaxel infusion on C1D7,D14 and again 30 minutes before infusion on C1D8,D15. Secondary objectives are to determine paclitaxel’s effect on PK of nilotinib and vice versa. Quality of life via CIPN-20 survey, disease free survival, event free survival, overall survival are exploratory objectives. NCT04205903 enrollment opened February 2020 and is accepting patients. Clinical trial information: NCT04205903 .

An adaptive trial design to optimize dose-schedule regimes with delayed outcomes.

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Abstract 791: Rational dose optimization for multi-drug cocktails

Abstract The design of an optimal combination therapy can be thought of as a set of tradeoffs designed to maximize the efficacy of the combination for a given toxicity budget. As anticancer agents (even targeted ones) often have overlapping toxicities, an efficient approach to making efficacy-toxicity tradeoffs is critical. In this context, a major impediment is that the search space grows exponentially with each additional drug while in vivo studies have a fixed upper size limit. Here, we model the efficacy and toxicity landscapes of multidrug cocktails, and describe an approach to visualize and design efficient studies for three-drug combinations. We further demonstrate the application of our approach in a practical drug development context by showing its impact on actual study design, and validation with in vivo datasets. As our approach relies on equations from first principles, it is in theory extensible to an arbitrary number of drugs, subject to the practical constraints of drug dosing. First, we demonstrate an approach to visually determine the optimal dose combination for a three-drug combination with fully overlapping toxicity. Efficacy and toxicity isoboles visualized as surfaces in three-drug space demonstrate the optimal dose combination, which corresponds to the point of tangency between the MTD and efficacy isobole surfaces. Next, we derive an analytical method to efficiently estimate this point with a minimum of data, and without using graphical methods (important for extending the work beyond three dimensions). The combined efficacy and toxicity of a three-drug combination were modeled from first principles using isobolograms as a sum of the single-agent PK/Efficacy (PK/E) relationships and four combination terms (three binary and one ternary) to account for potential interactions between the drugs. Borrowing from Microeconomic Utility Theory, we found the point of greatest efficacy along the Maximum Tolerated Dose (MTD) toxicity contour, which corresponds to the efficacy isobole tangentially intersecting the MTD contour. Through both analytical and numerical approaches, we determined the the single- and double-agent efficacy parameters uniquely determine the dose escalation path which provides the best estimate of combination efficacy. Simulated experiments demonstrate the optimal observation path up to MTD performs acceptably compared to a uniformly-gridded exposure space. Finally, we demonstrate the application of this approach in a proposed experimental design for three-drug combinations, and validate it with experimental data. While developing multi-drug cocktails poses many scientific and operational challenges, the approach presented here provides a straightforward route to preclinical testing and validation through the design of parsimonious studies that can explicitly define the contribution of each individual drug (and each two drug combination) to the overall efficacy and toxicity landscape of the cocktail. Citation Format: Christopher J. Zopf, Andrew Chen, Santhosh Palani, Rachael Brake, Mark Manfredi, Jeffrey Ecsedy, Wen Chyi Shyu, Arijit Chakravarty. Rational dose optimization for multi-drug cocktails. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 791. doi:10.1158/1538-7445.AM2014-791

SP0008 Critical Issues in the Management of Sle

Management of systemic lupus erythematosus (SLE) spans a quite broad spectrum of items including: patient assessment and monitoring, patient education, life style recommendations, treatment strategies, and others. For this reason...

Using Data Augmentation to Facilitate Conduct of Phase I–II Clinical Trials With Delayed Outcomes

A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I–II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design’s decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I–II design based on efficacy–toxicity trade-offs, including a computer stimulation study. Supplementary materials for this article are available online.

Practical implementation of an adaptive phase I/II design in chronic myeloid leukaemia: evaluating both efficacy and toxicity using the EffTox design

Despite known limitations of algorithm based designs (e.g. 3+3) they are favoured for use in dose finding studies due to simplicity and familiarity. Bayesian adaptive designs can overcome these limitations as they’re more efficient and substantially more accurate. EffTox is such a design which aims to determine the optimal, tolerable and efficacious dose. We implemented this design in a multicentre clinical trial of a novel TKI in combination with chemotherapy to treat chronic myeloid leukaemia. It is expected that the probability of efficacy may increase to a maximum then level off, thus higher doses of the combination may not result in greater efficacy. Given this non-monotone dose-response relationship, solely toxicity based dose-finding designs are not appropriate. In addition EffTox has demonstrated favourable properties of exposing fewer patients to potentially toxic and inefficacious doses, allocates more patients to the optimal dose and results in less dose-modifying toxicities in a subsequent phase-II/III trial. A Bayesian analysis is conducted using a bivariate binary probability model with prior probabilities elicited from clinicians. Dose recommendations are based on an efficacy-toxicity trade-off contour. Operating characteristics were explored via simulations of various scenarios using readily available EffTox software. The design has satisfactory operational characteristics and is flexible to be adapted trial specific requirements. Challenges in implementing this design included overcoming clinicians’ preconceptions, obtaining clinician consensus to enable development of the desirability trade-off contour and educating committees to secure funding. Overcoming these challenges allows design of robust dose-finding trials establishing an optimal, rather than sub-optimal dose.

Practical model-based dose finding in early-phase clinical trials: optimizing tissue plasminogen activator dose for treatment of ischemic stroke in children.

A safe and effective tissue plasminogen activator (tPA) dose for childhood stroke has not been established. This article describes a Bayesian outcome-adaptive method for determining the best dose of an experimental agent and explains how this method was used to design a dose-finding trial for tPA in childhood. The method assigns doses to successive cohorts of patients on the basis of each dose's desirability, quantified in terms of the tradeoff between efficacy and toxicity. The tradeoff function is constructed from several pairs of equally desirable (efficacy, toxicity) probabilities specified by the physicians planning the trial. Each cohort's dose is chosen adaptively, based on dose-outcome data from the patients treated previously in the trial, to optimize the efficacy-toxicity tradeoff. Application of the method to design the tPA trial is described, including a computer simulation study to establish design properties. A hypothetical cohort-by-cohort example is given to illustrate how the method works during trial conduct. Because only a dose that is both safe and efficacious may be selected and the method combines phase I and phase II by integrating efficacy and toxicity to choose doses, it avoids the more time-consuming and expensive conventional approach of conducting a phase I trial based on toxicity alone followed by a phase II trial based on efficacy alone. This is especially useful in settings with low accrual rates, such as trials of tPA for pediatric acute ischemic stroke.

Adaptive Dose Selection Using Efficacy-Toxicity Trade-Offs: Illustrations and Practical Considerations

The purpose of this paper is to describe and illustrate an outcome-adaptive Bayesian procedure, proposed by Thall and Cook (2004), for assigning doses of an experimental treatment to successive cohorts of patients. The method uses elicited (efficacy, toxicity) probability pairs to construct a family of trade-off contours that are used to quantify the desirability of each dose. This provides a basis for determining a best dose for each cohort. The method combines the goals of conventional Phase I and Phase II trials, and thus may be called a “Phase I-II” design. We first give a general review of the probability model and dose-finding algorithm. We next describe an application to a trial of a biologic agent for treatment of acute myelogenous leukemia, including a computer simulation study to assess the design's average behavior. To illustrate how the method may work in practice, we present a cohort-by-cohort example of a particular trial. We close with a discussion of some practical issues that may arise during implementation.

Dose-finding based on efficacy-toxicity trade-offs.

We present an adaptive Bayesian method for dose-finding in phase I/II clinical trials based on trade-offs between the probabilities of treatment efficacy and toxicity. The method accommodates either trinary or bivariate binary outcomes, as well as efficacy probabilities that possibly are nonmonotone in dose. Doses are selected for successive patient cohorts based on a set of efficacy-toxicity trade-off contours that partition the two-dimensional outcome probability domain. Priors are established by solving for hyperparameters that optimize the fit of the model to elicited mean outcome probabilities. For trinary outcomes, the new algorithm is compared to the method of Thall and Russell (1998, Biometrics 54, 251-264) by application to a trial of rapid treatment for ischemic stroke. The bivariate binary outcome case is illustrated by a trial of graft-versus-host disease treatment in allogeneic bone marrow transplantation. Computer simulations show that, under a wide rage of dose-outcome scenarios, the new method has high probabilities of making correct decisions and treats most patients at doses with desirable efficacy-toxicity trade-offs.

Disease-modifying antirheumatic drugs, including methotrexate, sulfasalazine, gold, antimalarials, and penicillamine.

Recently, there has been an interest in rethinking the classification of antirheumatic drugs. Emphasis continues to be on aggressive control of inflammation in the early phase of rheumatoid arthritis. The mistake of extrapolating short-term clinical trial results to long-term outcomes has been appreciated, pointing to the need for long-term studies. Interest in the role of cytokines and their receptors in the inflammatory process continues, as well as in the cellular mechanisms of action of the various disease-modifying antirheumatic drugs (DMARDs). Troublesome toxicity profiles continue to be reported, and a consideration of efficacy-toxicity trade-offs are important. Methotrexate still shows long-term efficacy, and low-dose folinic acid has been shown to reduce toxicity but not efficacy. New information on other DMARDs is presented, ie, sulfasalazine inhibition of signal transduction, the effects of hydroxychloroquine on cytokines and lipid metabolism, and the immunosuppressive effects of bucillamine, a penicillamine-related compound.