Nanoscale synthesis and affinity ranking.

Most drugs are developed through iterative rounds of chemical synthesis and biochemical testing to optimize the affinity of a particular compound for a protein target of therapeutic interest. This process is challenging because candidate molecules must be selected from a chemical space of more than 1060 drug-like possibilities 1 , and a single reaction used to synthesize each molecule has more than 107 plausible permutations of catalysts, ligands, additives and other parameters 2 . The merger of a method for high-throughput chemical synthesis with a biochemical assay would facilitate the exploration of this enormous search space and streamline the hunt for new drugs and chemical probes. Miniaturized high-throughput chemical synthesis3-7 has enabled rapid evaluation of reaction space, but so far the merger of such syntheses with bioassays has been achieved with only low-density reaction arrays, which analyse only a handful of analogues prepared under a single reaction condition8-13. High-density chemical synthesis approaches that have been coupled to bioassays, including on-bead 14 , on-surface 15 , on-DNA 16 and mass-encoding technologies 17 , greatly reduce material requirements, but they require the covalent linkage of substrates to a potentially reactive support, must be performed under high dilution and must operate in a mixture format. These reaction attributes limit the application of transition-metal catalysts, which are easily poisoned by the many functional groups present in a complex mixture, and of transformations for which the kinetics require a high concentration of reactant. Here we couple high-throughput nanomole-scale synthesis with a label-free affinity-selection mass spectrometry bioassay. Each reaction is performed at a 0.1-molar concentration in a discrete well to enable transition-metal catalysis while consuming less than 0.05 milligrams of substrate per reaction. The affinity-selection mass spectrometry bioassay is then used to rank the affinity of the reaction products to target proteins, removing the need for time-intensive reaction purification. This method enables the primary synthesis and testing steps that are critical to the invention of protein inhibitors to be performed rapidly and with minimal consumption of starting materials.

Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Harmonizing altered measures in integrative data analysis: A methods analogue study.

In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis (MNLFA) scores based on traditional indices of reliability (test-retest, internal, and score recovery or parallel forms) and validity. Participants in the analogue study included 854 college students (46% male; 21% African American, 5% Hispanic/Latino, 56% European American) who completed two versions of the altered measures at two sessions, separated by 2 weeks. As expected, mean, CFA, and MNLFA scores all resulted in scales with lower reliability given increasing scale alteration (with less fidelity to formerly developed scales) and shorter scale length. MNLFA and CFA scores, however, showed greater validity than mean scores, demonstrating stronger relationships with external correlates. Implications for measurement harmonization in the context of IDA are discussed.

Bidirectional Relations between Witnessing Violence, Victimization, Life Events, and Physical Aggression among Adolescents in Urban Schools.

Although there is empirical evidence supporting associations between exposure to violence and engaging in physically aggressive behavior during adolescence, there is limited longitudinal research to determine the extent to which exposure to violence is a cause or a consequence of physical aggression, and most studies have not addressed the influence of other negative life events experienced by adolescents. This study examined bidirectional relations between physical aggression, two forms of exposure to violence-witnessing violence and victimization, and other negative life events. Participants were a sample of 2568 adolescents attending three urban public middle schools who completed measures of each construct every 3 months during middle school. Their mean age was 12.76 (SD = 0.98); 52% were female. The majority were African American (89%); 17% were Hispanic or Latino/a. Cross-lagged regression analyses across four waves of data collected within the same grade revealed bidirectional relations between witnessing violence and physical aggression, and between witnessing violence and negative life events. Although physical aggression predicted subsequent changes in victimization, victimization predicted changes in physical aggression only when witnessing violence was not taken into account. Findings were consistent across sex and grades. Overall, these findings highlight the need for interventions that break the connection between exposure to violence and aggression during adolescence.

The Increasing Diversity and Complexity of Family Structures for Adolescents.

The structure of adolescents' families, and thus parental forms, in the United States, have become more heterogeneous and fluid over the past several decades. These changes are due to increases in never-married, single parents, divorce, cohabitation, same-sex parenting, multi-partnered fertility, and co-residence with grandparents. We document current diversity and complexity in adolescents' families as important context for rethinking future parenting theory and research. We also discuss how understandings of adolescents' families are somewhat limited by current methods used to measure characteristics of families. We recommend social network and profile-based methods as alternatives to capturing key dimensions of family structure and processes. Understanding the diversity of households and families in which adolescents are raised can improve theory and research on parenting.

A novel modeling framework for ordinal data defined by collapsed counts.

Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many response categories defined by collapsed count data (0 drinking days, 1-2 days, 3-6 days, etc.). Consequently, existing models ignore the underlying count process, resulting in disjoint between the construct of interest and the models being fitted. Our proposed ordinal modeling approach overcomes this limitation by explicitly linking ordinal responses to a suitable underlying count distribution. In doing so, researchers can use maximum likelihood estimation to fit count models to ordinal data as if they had directly observed the underlying discrete counts. The usefulness of our ordinal negative binomial and ordinal zero-inflated negative binomial models is verified by simulation studies. We also demonstrate our approach using real empirical data from the 2010 National Survey of Drug Use and Health. Results show the benefit of the proposed ordinal modeling framework compared with existing methods.

Modulating the interaction between CDK2 and cyclin A with a quinoline-based inhibitor.

A new class of quinoline-based kinase inhibitors has been discovered that both disrupt cyclin dependent 2 (CDK2) interaction with its cyclin A subunit and act as ATP competitive inhibitors. The key strategy for discovering this class of protein-protein disrupter compounds was to screen the monomer CDK2 in an affinity-selection/mass spectrometry-based technique and to perform secondary assays that identified compounds that bound only to the inactive CDK2 monomer and not the active CDK2/cyclin A heterodimer. Through a series of chemical modifications the affinity (Kd) of the original hit improved from 1 to 0.005µM.

A Moderated Nonlinear Factor Model for the Development of Commensurate Measures in Integrative Data Analysis.

Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across two or more independent sources. IDA offers many potential advantages including increased statistical power, greater subject heterogeneity, higher observed frequencies of low base-rate behaviors, and longer developmental periods of study. However, a core challenge is the estimation of valid and reliable psychometric scores that are based on potentially different items with different response options drawn from different studies. In Bauer and Hussong (2009) we proposed a method for obtaining scores within an IDA called moderated nonlinear factor analysis (MNLFA). Here we move significantly beyond this work in the development of a general framework for estimating MNLFA models and obtaining scale scores across a variety of settings. We propose a five step procedure and demonstrate this approach using data drawn from n=1972 individuals ranging in age from 11 to 34 years pooled across three independent studies to examine the factor structure of 17 binary items assessing depressive symptomatology. We offer substantive conclusions about the factor structure of depression, use this structure to compute individual-specific scale scores, and make recommendations for the use of these methods in practice.

Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Rapid Affinity and Microsomal Stability Ranking of Crude Mixture Libraries of Histone Deacetylase Inhibitors.

The science of drug discovery involves multiparameter optimization of molecular structures through iterative design-make-test cycles. For medicinal chemistry library synthesis, traditional workflows involve the isolation of each individual compound, gravimetric quantitation, and preparation of a standard concentration solution for biological assays. In this work, we explore ways to expedite this process by testing unpurified library mixtures using a combination of mass spectrometry-based assays for affinity selection and microsomal metabolic stability. Utilizing this approach, microgram quantities of crude library mixtures can be used to identify high affinity, metabolically stable library members for isolation and full characterization. This streamlined approach was demonstrated for the synthesis and evaluation of two libraries of histone deacetylase inhibitors and was shown to generate decision-making data in line with traditional workflows. The advantages of this paradigm include greatly reduced cycle time, reduced material requirements, and concentration of resources on the most promising compounds.

Integrative data analysis in clinical psychology research.

Integrative data analysis (IDA), a novel framework for conducting the simultaneous analysis of raw data pooled from multiple studies, offers many advantages including economy (i.e., reuse of extant data), power (i.e., large combined sample sizes), the potential to address new questions not answerable by a single contributing study (e.g., combining longitudinal studies to cover a broader swath of the lifespan), and the opportunity to build a more cumulative science (i.e., examining the similarity of effects across studies and potential reasons for dissimilarities). There are also methodological challenges associated with IDA, including the need to account for sampling heterogeneity across studies, to develop commensurate measures across studies, and to account for multiple sources of study differences as they impact hypothesis testing. In this review, we outline potential solutions to these challenges and describe future avenues for developing IDA as a framework for studies in clinical psychology.

Self-compassion and fear of compassion in the treatment of chronic depression: Mechanisms of change?

OBJECTIVE: The aim of this naturalistic process study was to investigate the relationship between self-compassion, fear of compassion from others, and depressive symptoms over the course of psychotherapy in patients with chronic depression. METHOD: A sample of 226 patients with chronic depression who received inpatient short-term psychodynamic psychotherapy (STPP) provided weekly self-report measures of self-compassion, fear of compassion, and depressive symptoms (Patient Health Questionnaire-9). Trivariate latent curve modeling with structured residuals was applied to investigate the between- and within-patient relationships among the variables. RESULTS: At the between-patient level, a significant positive correlation was found between slope of depression and the slope of fear of compassion. At the within-patient level, a lower than expected level of fear of compassion predicted a subsequent lower than expected level of depression (mean weekly effect size = 0.12), with a smaller reciprocal relationship (mean weekly effect size = 0.08). There was no significant within-patient effect of self-compassion predicting subsequent depression, but a significant effect of a lower than expected level of depression predicting a subsequent higher than expected level of self-compassion (mean weekly effect size = -0.13). No within-patient effect between self-compassion and fear of compassion was found. CONCLUSIONS: In the context of this study, it appears that fear of compassion may be a putative mechanism of change involved in alleviating depressive symptoms in patients with chronic depression treated with STPP. On the other hand, self-compassion appears to be an outcome of psychotherapy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The disaggregation of within-person and between-person effects in longitudinal models of change.

Longitudinal models are becoming increasingly prevalent in the behavioral sciences, with key advantages including increased power, more comprehensive measurement, and establishment of temporal precedence. One particularly salient strength offered by longitudinal data is the ability to disaggregate between-person and within-person effects in the regression of an outcome on a time-varying covariate. However, the ability to disaggregate these effects has not been fully capitalized upon in many social science research applications. Two likely reasons for this omission are the general lack of discussion of disaggregating effects in the substantive literature and the need to overcome several remaining analytic challenges that limit existing quantitative methods used to isolate these effects in practice. This review explores both substantive and quantitative issues related to the disaggregation of effects over time, with a particular emphasis placed on the multilevel model. Existing analytic methods are reviewed, a general approach to the problem is proposed, and both the existing and proposed methods are demonstrated using several artificial data sets. Potential limitations and directions for future research are discussed, and recommendations for the disaggregation of effects in practice are offered.

Emotional clarity and tolerance of emotional distress as mechanisms of change in short-term psychodynamic psychotherapy for chronic depression.

OBJECTIVE: The aim of this naturalistic process study was to investigate the relationship between emotional clarity and tolerance of emotional distress and depressive symptoms over the course of short-term psychodynamic psychotherapy for chronically depressed patients. METHOD: Weekly self-reports of emotional clarity, tolerance of emotional distress, and depressive symptoms (PHQ-9) were provided by 252 patients with chronic depression who were admitted to a 13-week inpatient treatment program. Latent curve modeling with structured residuals (LCM-SR) was applied to investigate the between- and within-person effects of week-to-week change in emotional clarity and tolerance of emotional distress as predictors of subsequent depression. The relationship between emotional clarity and tolerance of emotional distress was also investigated. RESULTS: At the within-person level, higher level of emotional clarity and tolerance of emotional distress predicted subsequent lower level of depression. A reciprocal relationship was found for tolerance of emotional distress (lower level of depression predicted subsequent level of tolerance emotional distress) but not for emotional clarity. No within-person effect between emotional clarity and tolerance of emotional distress was found. DISCUSSION: The results indicate that emotional clarity and tolerance of emotional distress may be mechanisms of change in short-term psychodynamic psychotherapy for chronic depression. The results are consistent with previous findings of the importance of emotional clarity and tolerance of emotional distress in psychotherapy. This study demonstrated the utility of LCM-SR as a method to identity mechanisms of change in psychotherapy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Molecular and behavioral consequences of Ube3a gene overdosage in mice.

Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome-based transgenic mouse models (Ube3aOE) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, Ube3aOE mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2-q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.

Discovery of novel imidazo[1,2-a]pyrazin-8-amines as Brk/PTK6 inhibitors.

A series of substituted imidazo[1,2-a]pyrazin-8-amines were discovered as novel breast tumor kinase (Brk)/protein tyrosine kinase 6 (PTK6) inhibitors. Tool compounds with low-nanomolar Brk inhibition activity, high selectivity towards other kinases and desirable DMPK properties were achieved to enable the exploration of Brk as an oncology target.

Psychometric Models for Scoring Multiple Reporter Assessments: Applications to Integrative Data Analysis in Prevention Science and Beyond.

Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter assessments, particularly when using integrative data analysis to fit models to data that have been pooled across two or more independent samples. The current paper uses both simulated and real data to examine the utility of a recently proposed psychometric model for multiple reporter data called the trifactor model (TFM) in settings that might be commonly found in prevention research. Results suggest that numerical scores obtained using the TFM are superior to more traditional methods, particularly when pooling samples that contribute different reporter perspectives.

Excess mortality during the COVID-19 pandemic: a geospatial and statistical analysis in Aden governorate, Yemen.

BACKGROUND: The burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May-June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records. METHODS: After identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period. RESULTS: We collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020. DISCUSSION: To our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

Discovery of imidazo[1,2-a]pyrazine-based Aurora kinase inhibitors.

The synthesis and structure-activity relationships (SAR) of novel, potent imidazo[1,2-a]pyrazine-based Aurora kinase inhibitors are described. The X-ray crystal structure of imidazo[1,2-a]pyrazine Aurora inhibitor 1j is disclosed. Compound 10i was identified as lead compound with a promising overall profile.

Discovery of orally bioavailable imidazo[1,2-a]pyrazine-based Aurora kinase inhibitors.

We report a series of potent imidazo[1,2-a]pyrazine-based Aurora kinase inhibitors. Optimization of the solvent accessible 8-position led to improvements in both oral bioavailability and off-target kinase inhibition. Compound 25 demonstrates anti-tumor activity in an A2780 ovarian tumor xenograft model.