Fluorophotometry is a sensitive method/technique to evaluate epithelial permeability in murine experimental dry eye

Purpose. Fluorophotometry is a well validated method for assessing corneal permeability in human subjects. However, with the growing importance of basic science animal research in ophthalmology, fluorophotometry’s use in animals must be further evaluated. The purpose of this study was to evaluate corneal epithelial permeability following desiccating stress using the modified Fluorotron Master™. ^ Methods. Corneal permeability was evaluated prior to and after subjecting 6-8 week old C57BL/6 mice to experimental dry eye (EDE) for 2 and 5 days (n=9/time point). Untreated mice served as controls. Ten microliters of 0.001% sodium fluorescein (NaF) were instilled topically into each mouse’s left eye to create an eye bath, and left to permeate for 3 minutes. The eye bath was followed by a generous wash with Buffered Saline Solution (BSS) and alignment with the Fluorotron Master™. Seven corneal scans using the Fluorotron Master were performed during 15 minutes (1 st post-wash scans), followed by a second wash using BSS and another set of five corneal scans (2nd post-wash scans) during the next 15 minutes. Corneal permeability was calculated using data calculated with the FM™ Mouse software. ^ Results. When comparing the difference between the Post wash #1 scans within the group and the Post wash #2 scans within the group using a repeated measurement design, there was a statistical difference in the corneal fluorescein permeability of the Post-wash #1 scans after 5 days (1160.21±108.26 vs. 1000.47±75.56 ng/mL, P<0.016 for UT-5 day comparison 8 [0.008]), but not after only 2 days of EDE compared to Untreated mice (1115.64±118.94 vs. 1000.47±75.56 ng/mL, P>0.016 for UT-2 day comparison [0.050]). There was no statistical difference between the 2 day and 5 day Post wash #1 scans (P=.299). The Post-wash #2 scans demonstrated that EDE caused a significant NaF retention at both 2 and 5 days of EDE compared to baseline, untreated controls (1017.92±116.25, 1015.40±120.68 vs. 528.22±127.85 ng/mL, P<0.05 [0.0001 for both]). There was no statistical difference between the 2 day and 5 day Post wash #2 scans (P=.503). The comparison between the Untreated post wash #1 with untreated post wash #2 scans using a Paired T-test showed a significant difference between the two sets of scans (P=0.000). There is also a significant difference between the 2 day comparison and the 5 day comparison (P values = 0.010 and 0.002, respectively). ^ Conclusion. Desiccating stress increases permeability of the corneal epithelium to NaF, and increases NaF retention in the corneal stroma. The Fluorotron Master is a useful and sensitive tool to evaluate corneal permeability in murine dry eye, and will be a useful tool to evaluate the effectiveness of dry eye treatments in animal-model drug trials.^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Behavioral facets of cancer chemoprevention trials

Few studies have explored factors related to participation in cancer chemoprevention trials. The purpose of this dissertation was to conduct investigations in this emerging field by studying aspects of participation at three phases of cancer chemoprevention trials: at enrollment, during a placebo run-in period, and post-trial. In all three studies, subjects had a history of cancer and were at high risk of recurrence or second primary tumors.^ The first study explored correlates of enrollment in a head and neck cancer chemoprevention trial by comparing participants and eligible nonparticipants. Of 148 subjects who met the trial's preliminary eligibility criteria, 40% enrolled. In multivariate analysis, enrollment was positively associated with being male (OR 2.36) and being employed (OR 2.73). The most commonly cited reason for declining participation among nonparticipants was transportation.^ The second study examined outcomes of an eight-week placebo run-in period in a head and neck cancer chemoprevention trial. Of 391 subjects, 91.3% were randomized after the run-in. Adherence to drug capsules ranged from 0% to 120.3% (mean $\pm$ SD, 95.8% $\pm$ 15.1). In multivariate analysis, the main variable predicting run-in outcome was race; white subjects were 3.45 times more likely to be randomized than non-white subjects. Subjects with Karnofsky scores of 100 were 2.13 times more likely to be randomized than were subjects with lower scores.^ The third study used post-trial questionnaires to assess subjects' (n = 64) perceptions of participation in a cancer chemoprevention trial. The most highly rated trial benefit was the perception of potential colon cancer prevention, and the most troublesome barrier was erroneous billing for study visits. Perceived benefits were positively associated with interest in participating in future trials of the same (p = 0.05) and longer (p = 0.02) duration, and difficulty with trial pills and procedures was inversely related to interest in future placebo-controlled trials (p = 0.01).^ These are among the first behavioral studies to be completed in the rapidly growing field of cancer chemoprevention. Much work has yet to be done, however, to advance our understanding of the complex issues relating to chemoprevention trial participation. ^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

Novel Use of Dual Anti-Inflammatory Therapy to Overcome Drug Resistance and Improve Functional Recovery Following Spinal Cord Injury

Over 1.2 million Americans are currently living with a traumatic spinal cord injury (SCI). Despite the need for effective therapies, there are currently no proven effective treatments that can improve recovery of function in SCI patients. Many therapeutic compounds have shown promise in preclinical models of SCI, but all of these have fallen short in clinical trials. P-glycoprotein (Pgp) is an active transporter expressed on capillary endothelial cell membranes at the blood-spinal cord barrier (BSCB). Pgp limits passive diffusion of blood-borne drugs into the CNS, by actively extruding drugs from the endothelial cell membrane. Pgp can become pathologically up-regulated, thus greatly impeding therapeutic drug delivery (‘multidrug resistance’). Importantly, many drugs that have been evaluated for the treatment of SCI are Pgp substrates. We hypothesized that Pgp-mediated drug resistance diminishes the delivery and efficacy of neuroprotective drugs following SCI. We observed a progressive, spatial spread of Pgp overexpression within the injured spinal cord. To assess Pgp function, we examined spinal cord uptake of systemically-delivered riluzole, a drug that is currently being evaluated in clinical trials as an SCI intervention. Blood-to-spinal cord riluzole penetration was reduced following SCI in wild-type but not Pgp-null rats, highlighting a critical role for Pgp in mediating spinal cord drug resistance after injury. Others have shown that pro-inflammatory signaling drives Pgp up-regulation in cancer and epilepsy. We have detected inflammation in both acutely- and chronically-injured spinal cord tissue. We therefore evaluated the ability of the dual COX-/5-LOX inhibitor licofelone to attenuate Pgp-mediated drug resistance following SCI. Licofelone treatment both reduced spinal cord Pgp levels and enhanced spinal cord riluzole bioavailability following SCI. Thus, we propose that licofelone may offer a new combinatorial treatment strategy to enhance spinal cord drug delivery following SCI. Additionally, we assessed the ability of licofelone, riluzole, or both to enhance recovery of locomotor function following SCI. We found that licofelone treatment conferred a significant improvement in hindlimb function that was sustained through the end of the study. In contrast, riluzole did not improve functional outcome. We therefore conclude that licofelone holds promise as a potential neuroprotective intervention for SCI.

A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

A comparison of adherence with minocycline treatment in Parkinson's disease and rheumatoid arthritis clinical trials

Background: Little is known about the effects on patient adherence when the same study drug is administered in the same dose in two populations with two different diseases in two different clinical trials. The Minocycline in Rheumatoid Arthritis (MIRA) trial and the NIH Exploratory Trials in Parkinson's disease (NET-PD) Futility Study I provide a unique opportunity to do the above and to compare methods measuring adherence. This study may increase understanding of the influence of disease and adverse events on patient adherence and will provide insights to investigators selecting adherence assessment methods in clinical trials of minocycline and other drugs in future.^ Methods: Minocycline adherence by pill count and the effect of adverse events was compared in the MIRA and NET-PD FS1 trials using multivariable linear regression. Within the MIRA trial, agreement between assay and pill count was compared. The association of adverse events with assay adherence was examined using multivariable logistic regression.^ Results: Adherence derived from pill count in the MIRA and NET-PD FS1 trials did not differ significantly. Adverse events potentially related to minocycline did not appear useful to predict minocycline adherence. In the MIRA trial, adherence measured by pill count appears higher than adherence measured by assay. Agreement between pill count and assay was poor (kappa statistic = 0.25).^ Limitations: Trial and disease are completely confounded and hence the independent effect of disease on adherence to minocycline treatment cannot be studied.^ Conclusion: Simple pill count may be preferred over assay in the minocycline clinical trials to measure adherence. Assays may be less sensitive in a clinical setting where appointments are not scheduled in relation to medication administration time, given assays depend on many pharmacokinetic and instrument-related factors. However, pill count can be manipulated by the patient. Another study suggested that self-report method is more sensitive than pill count method in differentiating adherence from non-adherence. An effect of medication-related adverse events on adherence could not be detected.^

Modified 3+3 design for phase I clinical trials

Phase I clinical trial is mainly designed to determine the maximum tolerated dose (MTD) of a new drug. Optimization of phase I trial design is crucial to minimize the number of enrolled patients exposed to unsafe dose levels and to provide reliable information to the later phases of clinical trials. Although it has been criticized about its inefficient MTD estimation, nowadays the traditional 3+3 method remains dominant in practice due to its simplicity and conservative estimation. There are many new designs that have been proven to generate more credible MTD estimation, such as the Continual Reassessment Method (CRM). Despite its accepted better performance, the CRM design is still not widely used in real trials. There are several factors that contribute to the difficulties of CRM adaption in practice. First, CRM is not widely accepted by the regulatory agencies such as FDA in terms of safety. It is considered to be less conservative and tend to expose more patients above the MTD level than the traditional design. Second, CRM is relatively complex and not intuitive for the clinicians to fully understand. Third, the CRM method take much more time and need statistical experts and computer programs throughout the trial. The current situation is that the clinicians still tend to follow the trial process that they are comfortable with. This situation is not likely to change in the near future. Based on this situation, we have the motivation to improve the accuracy of MTD selection while follow the procedure of the traditional design to maintain simplicity. We found that in 3+3 method, the dose transition and the MTD determination are relatively independent. Thus we proposed to separate the two stages. The dose transition rule remained the same as 3+3 method. After getting the toxicity information from the dose transition stage, we combined the isotonic transformation to ensure the monotonic increasing order before selecting the optimal MTD. To compare the operating characteristics of the proposed isotonic method and the other designs, we carried out 10,000 simulation trials under different dose setting scenarios to compare the design characteristics of the isotonic modified method with standard 3+3 method, CRM, biased coin design (BC) and k-in-a-row design (KIAW). The isotonic modified method improved MTD estimation of the standard 3+3 in 39 out of 40 scenarios. The improvement is much greater when the target is 0.3 other than 0.25. The modified design is also competitive when comparing with other selected methods. A CRM method performed better in general but was not as stable as the isotonic method throughout the different dose settings. The results demonstrated that our proposed isotonic modified method is not only easily conducted using the same procedure as 3+3 but also outperforms the conventional 3+3 design. It can also be applied to determine MTD for any given TTL. These features make the isotonic modified method of practical value in phase I clinical trials.^

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Clinical trials, informed consent, & emergency medicine: A systematic literature review

Background: In the United States, the Food and Drug Administration (FDA) regulates clinical trials. These regulations address good clinical practices as well as human subject protection (FDA, 2012). One of the most important legal and ethical concerns in clinical trials is informed consent. 21 CFR 50 governs human subjects research. Part 50.24 provides an emergency research exception to the informed consent requirement. Research was conducted to determine the appropriateness of this exception, whether the benefit justifies the exception, and its public health significance.^ Methods: A systematic literature review was conducted and articles were identified from peer-reviewed journals.^ Results: There is some variance in opinions regarding the appropriateness of the exception, but the literature reviewed found the study results of these trials justified the waiver.^ Conclusion: The exception to the informed consent requirement is likely appropriate and justified in emergency research when implemented within the specified guidelines.^