A utility approach to individualized optimal dose selection using biomarkers

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.     

Utility‐Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II Trials

Summary An outcome‐adaptive Bayesian design is proposed for choosing the optimal dose pair of a chemotherapeutic agent and a biological agent used in combination in a phase I/II clinical trial. Patient outcome is characterized as a vector of two ordinal variables accounting for toxicity and treatment efficacy. A generalization of the Aranda‐Ordaz model (1981, Biometrika 68 , 357–363) is used for the marginal outcome probabilities as functions of a dose pair, and a Gaussian copula is assumed to obtain joint distributions. Numerical utilities of all elementary patient outcomes, allowing the possibility that efficacy is inevaluable due to severe toxicity, are obtained using an elicitation method aimed to establish consensus among the physicians planning the trial. For each successive patient cohort, a dose pair is chosen to maximize the posterior mean utility. The method is illustrated by a trial in bladder cancer, including simulation studies of the method's sensitivity to prior parameters, the numerical utilities, correlation between the outcomes, sample size, cohort size, and starting dose pair.     

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: Application to a dose-finding trial for a novel Russell’s viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. fewer than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined ‘optimal dose’. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russell’s viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Daboia siamensis envenoming, efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified ‘3+3’ design). The model based design can identify an optimal dose after fewer patients relative to the rule based design. Open source code for the simulations is made available in order to determine adequate sample sizes for future adaptive snakebite trials. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar.     

Patient-specific dose finding based on bivariate outcomes and covariates

SUMMARY: A Bayesian sequential dose-finding procedure based on bivariate (efficacy, toxicity) outcomes that accounts for patient covariates and dose-covariate interactions is presented. Historical data are used to obtain an informative prior on covariate main effects, with uninformative priors assumed for all dose effect parameters. Elicited limits on the probabilities of efficacy and toxicity for each of a representative set of covariate vectors are used to construct bounding functions that determine the acceptability of each dose for each patient. Elicited outcome probability pairs that are equally desirable for a reference patient are used to define two different posterior criteria, either of which may be used to select an optimal covariate-specific dose for each patient. Because the dose selection criteria are covariate specific, different patients may receive different doses at the same point in the trial, and the set of eligible patients may change adaptively during the trial. The method is illustrated by a dose-finding trial in acute leukemia, including a simulation study.     

A parallel phase I/II clinical trial design for combination therapies

The use of multiple drugs in a single clinical trial or as a therapeutic strategy has become common, particularly in the treatment of cancer. Because traditional trials are designed to evaluate one agent at a time, the evaluation of therapies in combination requires specialized trial designs. In place of the traditional separate phase I and II trials, we propose using a parallel phase I/II clinical trial to evaluate simultaneously the safety and efficacy of combination dose levels, and select the optimal combination dose. The trial is started with an initial period of dose escalation, then patients are randomly assigned to admissible dose levels. These dose levels are compared with each other. Bayesian posterior probabilities are used in the randomization to adaptively assign more patients to doses with higher efficacy levels. Combination doses with lower efficacy are temporarily closed and those with intolerable toxicity are eliminated from the trial. The trial is stopped if the posterior probability for safety, efficacy, or futility crosses a prespecified boundary. For illustration, we apply the design to a combination chemotherapy trial for leukemia. We use simulation studies to assess the operating characteristics of the parallel phase I/II trial design, and compare it to a conventional design for a standard phase I and phase II trial. The simulations show that the proposed design saves sample size, has better power, and efficiently assigns more patients to doses with higher efficacy levels.     

A variance-stabilizing transformation for gene-expression microarray data

MOTIVATION: Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background. RESULTS: We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data.     

A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as “optimal,” which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.     

Easy-to-implement Bayesian methods for dose-escalation studies in healthy volunteers

In phase I clinical trials, experimental drugs are administered to healthy volunteers in order to establish their safety and to explore the relationship between the dose taken and the concentration found in plasma. Each volunteer receives a series of increasing single doses. In this paper a Bayesian decision procedure is developed for choosing the doses to give in the next round of the study, taking into account both prior information and the responses observed so far. The procedure seeks the optimal doses for learning about the dose-concentration relationship, subject to a constraint which reduces the risk of administering dangerously high doses. Individual volunteers receive more than one dose, and the pharmacokinetic responses observed are, after logarithmic transformation, treated as approximately normally distributed. Thus data analysis can be achieved by fitting linear mixed models. By expressing prior information as 'pseudo-data', and by maximizing over posterior distributions rather than taking expectations, a procedure which can be implemented using standard mixed model software is derived. Comparisons are made with existing approaches to the conduct of these studies, and the new method is illustrated using real and simulated data.To whom correspondence should be addressed.     

Weighting functions and data truncation in the fitting of multi-exponential functions (Short Communication)

Artificial data were simulated by using two-exponential functions and normally distributed pseudo-random numbers. The variation corresponded to either constant or relative variance for the data error. These data were used to test (i) three different weighting functions and (ii) the effect of data truncation on the precision of estimating the parameters of two-exponential functions.     

In vivo estrogenic comparisons of Trifolium pratense (red clover) Humulus lupulus (hops), and the pure compounds isoxanthohumol and 8-prenylnaringenin

The lack of a safe and reliable alternative to hormone therapy (HT) for treating menopausal symptoms underscores the need for alternative therapies. OBJECTIVE: The purpose of this study was to assess the in vivo estrogenic effects of the botanical dietary supplements Trifolium pratense (red clover) and Humulus lupulus (hops), and two compounds obtained from H. lupulus, isoxanthohumol and 8-prenylnaringenin (8-PN) using the ovariectomized uterotrophic adult rat model. A H. lupulus extract and a 30% isoflavone extract of T. pratense were tested at three escalating doses as was one dose of isoxanthohumol for 21d. 8-Prenylnaringenin, the major estrogen in H. lupulus, was also tested at three relevant escalating doses. In order to determine the in vivo metabolism of 8-PN, the major phases I and II metabolites were also identified. The primary outcome measure, uterus weight gain, indicated that H. lupulus and T. pratense did not have an estrogenic effect on the uterus, and none of the secondary outcome measures were positive. In contrast, there was a clear dose response when 8-PN was evaluated where the middle and high doses of 8-PN were active. 8-Prenylnaringenin in rat plasma, liver, and mammary gland was measured and the major phases I and II 8-PN metabolites were detected. Our findings suggest that while both the H. lupulus and T. pratense extracts do not have an effect on the rat uterus, 8-PN at equivalent doses to those previously used in humans did have an effect, and may therefore have a deleterious effect in women.     

N-terminal truncation of antiapoptotic MCL1, but not G2/M-induced phosphorylation, is associated with stabilization and abundant expression in tumor cells

The antiapoptotic BCL2 family member MCL1 is normally up- and down-modulated in response to environmental signals and conditions, but is constitutively expressed in cancer where it promotes cell survival and drug resistance. A post-translational modification identified here, truncation at the N terminus, was found to act along with previously described ERK- and GSK3-induced phosphorylation events to regulate the turnover of the MCL1 protein and thus its availability for antiapoptotic effects. Although both N-terminally truncated and full-length MCL1 contain sequences enriched in proline, glutamic acid, serine, and threonine and were susceptible to proteasomal degradation, the truncated form decayed less rapidly and was maintained for an extended period in the presence of ERK activation. This was associated with extended cell survival because the truncated form of MCL1 (unlike those of BCL2 and BCLX) retained antiapoptotic activity. N-terminal truncation slightly increased the electrophoretic mobility of MCL1 and differed from the phosphorylation/band shift to decreased mobility, which occurs in the G2/M phase and was not found to affect MCL1 turnover. The N-terminally truncated form of MCL1 was expressed to varying extents in normal lymphoid tissues and was the predominant form present in lymphomas from transgenic mice and human tumor lines of B-lymphoid origin. The degradation versus stabilized expression of antiapoptotic MCL1 is thus controlled by N-terminal truncation as well as by ERK- and GSK3 (but not G2/M)-induced phosphorylation. These modifications may contribute to dysregulated MCL1 expression in cancer and represent targets for promoting its degradation to enhance tumor cell death.     

Invasion speeds for structured populations in fluctuating environments

We live in a time where climate models predict future increases in environmental variability and biological invasions are becoming increasingly frequent. A key to developing effective responses to biological invasions in increasingly variable environments will be estimates of their rates of spatial spread and the associated uncertainty of these estimates. Using stochastic, stage-structured, integrodifference equation models, we show analytically that invasion speeds are asymptotically normally distributed with a variance that decreases in time. We apply our methods to a simple juvenile–adult model with stochastic variation in reproduction and an illustrative example with published data for the perennial herb, Calathea ovandensis. These examples buttressed by additional analysis reveal that increased variability in vital rates simultaneously slow down invasions yet generate greater uncertainty about rates of spatial spread. Moreover, while temporal autocorrelations in vital rates inflate variability in invasion speeds, the effect of these autocorrelations on the average invasion speed can be positive or negative depending on life history traits and how well vital rates “remember” the past.     

Biometric Evaluation of the ATC Method for the Determination of the Acute Dermal Toxicity of Chemicals

The new dermal acute toxic class (ATC) method is presented for one specific classification system for chemicals (released by the European Union) according to acute dermal toxicity. It is a stepwise procedure using three animals of one sex per step. Three starting doses are possible. Assuming a Probit model for the dose response relationship probabilities of a correct, of a less and of a more stringent classification are calculated. It is shown that these probabilities depend only weakly on the starting dose. Also, the expected numbers of used and of dead animals are derived in dependence on the LD₅₀ and on the dose response slope β, as well as the starting doses minimizing the expected animal number are proposed. The results demonstrate that the dermal ATC method is a reliable alternative to the classical LD₅₀ test with the use of significantly fewer animals.     

Treatment comparisons based on two-dimensional safety and efficacy alternatives in oncology trials

In addition to their desired anticancer effects, most cancer treatments may also cause transient toxicity, permanent organ damage, or death. A critical question in comparing an experimental treatment to a standard is how much increase in an adverse event rate is an acceptable trade-off for achieving a targeted improvement in efficacy, or vice versa. We consider settings where one may characterize patient outcome as a bivariate (efficacy, safety) variable and quantify treatment effect as a corresponding two-dimensional parameter. A set of target parameters, each representing a clinically meaningful improvement over the standard, are elicited from the physician. Each target is a two-dimensional generalization of the usual one-dimensional shift parameter. We define the alternative hypothesis in the two-dimensional effect space as the convex hull of the sets of parameters that are at least as desirable as each target point. The rejection region is obtained by shifting the alternative toward (0,0) to achieve a given type I error, with sample size computed to achieve a given power at the targets. The method is illustrated by application to two cancer chemotherapy trials.     

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.     

The importance of appropriate controls, animal feed, and animal models in interpreting results from low-dose studies of bisphenol A

Interpreting results of studies that report only negative effects is problematic. A number of published studies to determine whether chemicals with estrogenic activity can cause effects at low doses have not taken into account the possibility that the commercial animal feed being used can mask effects of even potent estrogenic drugs such as diethylstilbestrol (DES). In addition, the sensitivity of the strain of animal being used for the specific category of chemical being tested has not always been described. For environmental chemicals, such as the estrogenic polycarbonate plastic monomer bisphenol A, DES is an appropriate positive control for estrogenic effects, and using an appropriate low dose of DES can eliminate the possibility of false-negative conclusions of safety when the above or other variables contribute to the negative outcome. Only when simultaneous positive effects of low doses of a positive control chemical such as DES and negative effects of environmentally relevant low doses of the test chemical are demonstrated within the same experiment are conclusions of no effect of the test chemical warranted, and this has not been reported for bisphenol A in any study. Instead, more than 90 refereed journal publications have reported effects due to exposure to low doses of bisphenol A in a wide variety of animals (for references see: http://rcp.missouri.edu/endocrinedisruptors/vomsaal/vomsaal.html). However, due to lack of attention to the importance of appropriate positive controls, a small number of studies reporting negative effects of bisphenol A have created a false sense of controversy regarding low-dose effects of bisphenol A.     

A new method for dealing with measurement error in explanatory variables of regression models

We introduce a new method, moment reconstruction, of correcting for measurement error in covariates in regression models. The central idea is similar to regression calibration in that the values of the covariates that are measured with error are replaced by "adjusted" values. In regression calibration the adjusted value is the expectation of the true value conditional on the measured value. In moment reconstruction the adjusted value is the variance-preserving empirical Bayes estimate of the true value conditional on the outcome variable. The adjusted values thereby have the same first two moments and the same covariance with the outcome variable as the unobserved "true" covariate values. We show that moment reconstruction is equivalent to regression calibration in the case of linear regression, but leads to different results for logistic regression. For case-control studies with logistic regression and covariates that are normally distributed within cases and controls, we show that the resulting estimates of the regression coefficients are consistent. In simulations we demonstrate that for logistic regression, moment reconstruction carries less bias than regression calibration, and for case-control studies is superior in mean-square error to the standard regression calibration approach. Finally, we give an example of the use of moment reconstruction in linear discriminant analysis and a nonstandard problem where we wish to adjust a classification tree for measurement error in the explanatory variables.     

First-In-Class Small Molecule ONC201 Induces DR5 and Cell Death in Tumor but Not Normal Cells to Provide a Wide Therapeutic Index as an Anti-Cancer Agent

We previously identified ONC201 (TIC10) as a first-in-class orally active small molecule with robust antitumor activity that is currently in clinical trials in advanced cancers. Here, we further investigate the safety characteristics of ONC201 in preclinical models that reveal an excellent safety profile at doses that exceed efficacious doses by 10-fold. In vitro studies indicated a strikingly different dose-response relationship when comparing tumor and normal cells where maximal effects are much stronger in tumor cells than in normal cells. In further support of a wide therapeutic index, investigation of tumor and normal cell responses under identical conditions demonstrated large apoptotic effects in tumor cells and modest anti-proliferative effects in normal cells that were non-apoptotic and reversible. Probing the underlying mechanism of apoptosis indicated that ONC201 does not induce DR5 in normal cells under conditions that induce DR5 in tumor cells; DR5 is a pro-apoptotic TRAIL receptor previously linked to the anti-tumor mechanism of ONC201. GLP toxicology studies in Sprague-Dawley rats and beagle dogs at therapeutic and exaggerated doses revealed no dose-limiting toxicities. Observations in both species at the highest doses were mild and reversible at doses above 10-fold the expected therapeutic dose. The no observed adverse event level (NOAEL) was ≥42 mg/kg in dogs and ≥125 mg/kg in rats, which both correspond to a human dose of approximately 1.25 g assuming standard allometric scaling. These results provided the rationale for the 125 mg starting dose in dose escalation clinical trials that began in 2015 in patients with advanced cancer.     

Fractioned Dose Regimen of Sunitinib for Patients with Gastrointestinal Stromal Tumor: A Pharmacokinetic and Treatment Efficacy Study

AIM: Sunitinib has shown benefit in patients with imatinib (IM)–resistant gastrointestinal stromal tumor (GIST). However, its advantages are somewhat diminished because of associated toxicities. Herein, we clarify the efficacy and safety of fractioned dose regimen of sunitinib by a pharmacokinetic and efficacy study. MATERIALS AND METHODS: Between 2001 and March 2013, a total of 214 patients with metastatic GIST was treated at Chang Gung Memorial Hospital. Among them, 55 (11.6%) patients who received sunitinib were investigated. One group of patients was administered with standard dose of once-daily sunitinib (standard dose group) and the other group was administered with standard total daily dose of sunitinib in fractioned doses (fractioned dose group). RESULTS: Thirty-two male and 23 female patients with a median age of 55 years received sunitinib. The median duration of sunitinib administration was 9.2 months. The clinical benefit was 65.2%. The mean peak blood level of sunitinib in patients with fractioned doses was significantly lower than that in those with once-daily dose (83.4 vs 50.1 ng/ml, P = .01). The rates of adverse effects of hand-foot syndrome, mucositis, and yellow skin were significantly decreased by fractioned doses of sunitinib. However, the progression-free and overall survival did not differ between patients with different treatment regimens. CONCLUSION: The fractioned dose regimen of sunitinib appears to be a safe and effective treatment for patients with IM-resistant/intolerant GISTs. Significantly decreased toxicity of this regimen could be explained by significantly lower peak sunitinib blood level. However, the treatment efficacy is not reduced by this regimen.