A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

An Extended Hierarchical Task Analysis for Error Prediction in Medical Devices

This paper introduces an extended hierarchical task analysis (HTA) methodology devised to evaluate and compare user interfaces on volumetric infusion pumps. The pumps were studied along the dimensions of overall usability and propensity for generating human error. With HTA as our framework, we analyzed six pumps on a variety of common tasks using Norman’s Action theory. The introduced method of evaluation divides the problem space between the external world of the device interface and the user’s internal cognitive world, allowing for predictions of potential user errors at the human-device level. In this paper, one detailed analysis is provided as an example, comparing two different pumps on two separate tasks. The results demonstrate the inherent variation, often the cause of usage errors, found with infusion pumps being used in hospitals today. The reported methodology is a useful tool for evaluating human performance and predicting potential user errors with infusion pumps and other simple medical devices.

Cost-effectiveness of universal prophylaxis in pregnancy with prior group B streptococci colonization.

OBJECTIVE: To estimate the costs and outcomes of rescreening for group B streptococci (GBS) compared to universal treatment of term women with history of GBS colonization in a previous pregnancy. STUDY DESIGN: A decision analysis model was used to compare costs and outcomes. Total cost included the costs of screening, intrapartum antibiotic prophylaxis (IAP), treatment for maternal anaphylaxis and death, evaluation of well infants whose mothers received IAP, and total costs for treatment of term neonatal early onset GBS sepsis. RESULTS: When compared to screening and treating, universal treatment results in more women treated per GBS case prevented (155 versus 67) and prevents more cases of early onset GBS (1732 versus 1700) and neonatal deaths (52 versus 51) at a lower cost per case prevented ($8,805 versus $12,710). CONCLUSION: Universal treatment of term pregnancies with a history of previous GBS colonization is more cost-effective than the strategy of screening and treating based on positive culture results.

A Hierarchical Bayesian Model for Measuring Motion Adaptation

Previous research has shown that motion imagery draws on the same neural circuits that are involved in perception of motion, thus leading to a motion aftereffect (Winawer et al., 2010). Imagined stimuli can induce a similar shift in participants’ psychometric functions as neural adaptation due to a perceived stimulus. However, these studies have been criticized on the grounds that they fail to exclude the possibility that the subjects might have guessed the experimental hypothesis, and behaved accordingly (Morgan et al., 2012). In particular, the authors claim that participants can adopt arbitrary response criteria, which results in similar changes of the central tendency μ of psychometric curves as those shown by Winawer et al. (2010).

The Effect Of Misleading Prior Information On The Subjective Visual Vertical

When tilted sideways participants misperceive the visual vertical assessed by means of a luminous line in otherwise complete dark- ness. A recent modeling approach (De Vrijer et al., 2009) claimed that these typical patterns of errors (known as A- and E-effects) could be explained by as- suming that participants behave in a Bayes optimal manner. In this study, we experimentally manipulate participants’ prior information about body-in-space orientation and measure the effect of this manipulation on the subjective visual vertical (SVV). Specifically, we explore the effects of veridical and misleading instructions about body tilt orientations on the SVV. We used a psychophys- ical 2AFC SVV task at roll tilt angles of 0 degrees, 16 degrees and 4 degrees CW and CCW. Participants were tilted to 4 degrees under different instruction conditions: in one condition, participants received veridical instructions as to their tilt angle, whereas in another condition, participants received the mis- leading instruction that their body position was perfectly upright. Our results indicate systematic differences between the instruction conditions at 4 degrees CW and CCW. Participants did not simply use an ego-centric reference frame in the misleading condition; instead, participants’ estimates of the SVV seem to lie between their head’s Z-axis and the estimate of the SVV as measured in the veridical condition. All participants displayed A-effects at roll tilt an- gles of 16 degrees CW and CCW. We discuss our results in the context of the Bayesian model by De Vrijer et al. (2009), and claim that this pattern of re- sults is consistent with a manipulation of precision of a prior distribution over body-in-space orientations. Furthermore, we introduce a Bayesian Generalized Linear Model for estimating parameters of participants’ psychometric function, which allows us to jointly estimate group level and individual level parameters under all experimental conditions simultaneously, rather than relying on the traditional two-step approach to obtaining group level parameter estimates.

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

High-speed rotational atherectomy before paclitaxel-eluting stent implantation in complex calcified coronary lesions: the randomized ROTAXUS (Rotational Atherectomy Prior to Taxus Stent Treatment for Complex Native Coronary Artery Disease) trial

OBJECTIVES This study sought to determine the effect of rotational atherectomy (RA) on drug-eluting stent (DES) effectiveness. BACKGROUND DES are frequently used in complex lesions, including calcified stenoses, which may challenge DES delivery, expansion, and effectiveness. RA can adequately modify calcified plaques and facilitate stent delivery and expansion. Its impact on DES effectiveness is widely unknown. METHODS The ROTAXUS (Rotational Atherectomy Prior to TAXUS Stent Treatment for Complex Native Coronary Artery Disease) study randomly assigned 240 patients with complex calcified native coronary lesions to RA followed by stenting (n = 120) or stenting without RA (n = 120, standard therapy group). Stenting was performed using a polymer-based slow-release paclitaxel-eluting stent. The primary endpoint was in-stent late lumen loss at 9 months. Secondary endpoints included angiographic and strategy success, binary restenosis, definite stent thrombosis, and major adverse cardiac events at 9 months. RESULTS Despite similar baseline characteristics, significantly more patients in the standard therapy group were crossed over (12.5% vs. 4.2%, p = 0.02), resulting in higher strategy success in the rotablation group (92.5% vs. 83.3%, p = 0.03). At 9 months, in-stent late lumen loss was higher in the rotablation group (0.44 ± 0.58 vs. 0.31 ± 0.52, p = 0.04), despite an initially higher acute lumen gain (1.56 ± 0.43 vs. 1.44 ± 0.49 mm, p = 0.01). In-stent binary restenosis (11.4% vs. 10.6%, p = 0.71), target lesion revascularization (11.7% vs. 12.5%, p = 0.84), definite stent thrombosis (0.8% vs. 0%, p = 1.0), and major adverse cardiac events (24.2% vs. 28.3%, p = 0.46) were similar in both groups. CONCLUSIONS Routine lesion preparation using RA did not reduce late lumen loss of DES at 9 months. Balloon dilation with only provisional rotablation remains the default strategy for complex calcified lesions before DES implantation.

Effect of ticagrelor on the outcomes of patients with prior coronary artery bypass graft surgery: insights from the PLATelet inhibition and patient outcomes (PLATO) trial

BACKGROUND Patients with prior coronary artery bypass graft surgery (CABG) who present with an acute coronary syndrome have a high risk for recurrent events. Whether intensive antiplatelet therapy with ticagrelor might be beneficial compared with clopidogrel is unknown. In this substudy of the PLATO trial, we studied the effects of randomized treatment dependent on history of CABG. METHODS Patients participating in PLATO were classified according to whether they had undergone prior CABG. The trial's primary and secondary end points were compared using Cox proportional hazards regression. RESULTS Of the 18,613 study patients, 1,133 (6.1%) had prior CABG. Prior-CABG patients had more high-risk characteristics at study entry and a 2-fold increase in clinical events during follow-up, but less major bleeding. The primary end point (composite of cardiovascular death, myocardial infarction, and stroke) was reduced to a similar extent by ticagrelor among patients with (19.6% vs 21.4%; adjusted hazard ratio [HR], 0.91 [0.67, 1.24]) and without (9.2% vs 11.0%; adjusted HR, 0.86 [0.77, 0.96]; P(interaction) = .73) prior CABG. Major bleeding was similar with ticagrelor versus clopidogrel among patients with (8.1% vs 8.7%; adjusted HR, 0.89 [0.55, 1.47]) and without (11.8% vs 11.4%; HR, 1.08 [0.98, 1.20]; P(interaction) = .46) prior CABG. CONCLUSIONS Prior-CABG patients presenting with acute coronary syndrome are a high-risk cohort for death and recurrent cardiovascular events but have a lower risk for major bleeding. Similar to the results in no-prior-CABG patients, ticagrelor was associated with a reduction in ischemic events without an increase in major bleeding.

Hierarchical self-assembly of nucleotide-appended oligopyrenotides into defined supramolecular objects

Supramolecular DNA assembly blends DNA building blocks with synthetic organic and inorganic molecules giving structural and functional advantages both to the initial self-assembly process and to the final construct. Synthetic molecules can bring a number of additional interactions into DNA nanotechnology. Incorporating extended aromatic molecules as connectors of DNA strands allows folding of these strands through π-π stacking (DNA “foldamers”). In previous work it was shown that short oligopyrenotides (phosphodiester-linked pyrene oligomers) behave as staircase-like foldamers, which cooperatively self-assemble into two-dimensional supramolecular polymers in aqueous medium. Herein, we demonstrate that a 10-mer DNA-sequence modified with 7 pyrene units (see illustration) forms dimensionally-defined supramolecular polymers under thermodynamic conditions in water. We present the self-assembly behavior, morphological studies, and the spectroscopic properties of the investigated DNA-sequences (illustrative AFM picture shown below).

Hierarchical self-assembly of nucleotide-appended oligopyrenotides into defined supramolecular objects

Supramolecular DNA assembly blends DNA building blocks with synthetic organic molecules giving structural and functional advantages. Incorporating extended aromatic molecules as connectors of DNA strands allows folding of these strands through π-π stacking (DNA 'foldamers'). In previous work it was shown that short oligopyrenotides behave as staircase-like foldamers, which cooperatively self-assemble into 2D supramolecular polymers in aqueous medium. Herein, we demonstrate that 10-mer DNA-sequence conjugated with seven pyrene unites forms dimensionally-defined supramolecular polymers under thermodynamic conditions in water. We present the self-assembly behavior, morphologycal studies (AFM and TEM), and the spectroscopic properties (UV/vis, CD) of the investigated pyrene - conjugated DNA-sequence.