Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions

Genome-wide association study of patient and clinician rated global impression severity during antipsychotic treatment

Examine the unique and congruent findings between multiple raters in a genome-wide association studies (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia

Investigation of the impact of cleaning on the adhesive bond and the process implications

While surface cleaning is the most common process step in DOE manufacturing operations, the link between a successful adhesive bond and the surface clean performed before adhesion is not well understood. An innovative approach that combines computer modeling expertise, fracture mechanics understanding, and cleaning experience to address how to achieve a good adhesive bond is discussed here to develop a capability that would result in reduced cleaning development time and testing, improved bonds, improved manufacturability, and even an understanding that leads to improved aging. A simulation modeling technique, polymer reference interaction site model applied near wall (Wall PRISM), provided the capability to include contaminants on the surface. Calculations determined an approximately 8% reduction in the work of adhesion for 1% by weight of ethanol contamination on the structure of a silicone adhesive near a surface. The demonstration of repeatable coatings and quantitative analysis of the surface for deposition of controlled amounts of contamination (hexadecane and mineral oil) was based on three deposition methods. The effect of the cleaning process used on interfacial toughness was determined. The measured interfacial toughness of samples with a Brulin cleaned sandblasted aluminum surface was found to be {approximately} 15% greater than that with a TCE cleaned aluminum surface. The sensitivity of measured fracture toughness to various test conditions determined that both interfacial toughness and interface corner toughness depended strongly on surface roughness. The work of adhesion value for silicone/silicone interface was determined by a contact mechanics technique known as the JKR method. Correlation with fracture data has allowed a better understanding between interfacial fracture parameters and surface energy

Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D.

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient's unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient's unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions

F-tests of the marginal effects of unique MDS dimensions and self-reported ethnicity.

<p>MDS – multidimensional scaling.</p><p>For both samples, in more models than expected by chance the marginal effect of the MDS dimensions significantly improved model fit, over and above self-reported ethnicity. Self-reported ethnicity showed mixed statistical evidence of improving model fit conditional on the MDS dimensions.</p

Summary of the average multiple regression correlation coefficients for the treatment-outcome combinations.

<p>PANSS – Positive and Negative Syndrome Scale. QTc - QT interval corrected for heart rate. QIDS – Quick Inventory of Depressive Symptomatology.</p

Number of subjects assessed and mean number of observations for each treatment-phenotype comparison. We present only a summary of phenotypes tested in this study.

<p>PANSS – Positive and Negative Syndrome Scale. QTc - QT interval corrected for heart rate. QIDS – Quick Inventory of Depressive Symptomatology.</p

Quantile-quantile (Q-Q) plots for the joint effects of MDS dimensions on various drug response measures.

<p>Points represent ordered model fit F-test -log₁₀(<i>p-</i>values), each of which quantifies the explanatory power of a model of 5 MDS ancestral dimensions predicting a measure of treatment response. The straight, dark grey lines represent the expected <i>p</i>-value distribution under the null hypothesis of no true associations. Light grey lines represent 95% confidence intervals for rejecting the null hypothesis at each <i>p</i>-value rank. The inflation parameter, lambda, is defined as the ratio of the median observed test statistic to the expected median under the null distribution; thus, lambda quantifies the degree to which the test statistic distribution systematically diverges from the null expectation of no significant effects.</p