The focus in the field of machine learning has increasingly shifted towards data-centric approaches, recognizing that the quality of data is crucial for the effectiveness of the models developed. One significant challenge that can degrade data quality is the presence of outliers. Therefore, this study investigates the impact of various outlier detection algorithms on the performance of machine learning models applied to QSAR datasets. Utilizing methods such as Isolation Forest (IF), Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM), the aim was to explore these methods and evaluate them, identify potential outliers, and assess their influence on model predictions. The study incorporated both synthetic data and real-world datasets, including those obtained from a pharmaceutical company and benchmark datasets. The methodology involved preprocessing the data, applying outlier detection algorithms, and evaluating the models using traditional metrics like Mean Squared Error (MSE) and conformal prediction for uncertainty estimation. Results indicated that no major improvements were observed using the different algorithms and that excessive data removal led to a decline. While OCSVM showed inconsistent performance across different datasets, LOF demonstrated promising potential as a method worth further investigation. This study has even highlighted different challenges including high dimensionality, the need for hyperparameter tuning, and the limitations of current outlier detection methods. It also underscores the complexity of outlier detection in QSAR data and suggests directions for future research to improve model robustness and accuracy.

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Background: Attentiveness is a fundamental function of the brain. Deficits in attentional performance is a major cause for individual and societal burden. An important region involved in attention is striatum, inside of which there is dopamine: a key neurotransmitter in the regulation of attention. The dopamine transporter (DAT) is abundant in dorsal striatum (DS) and ventral striatum (VS) and DAT acts as a modulator of dopamine signaling. Abnormal DAT expression is identified as one of the causes of attentional dysfunction in several neuropsychiatric diseases. This project aimed to parse the relationship between DAT expression and attentional performance in a sustained visual attention task, as well as DAT’s relationship to amphetamine (AMPH) sensitivity. Methods: To test for attention 15 male Sprague-Dawley rats were trained and tested in a signal detection task (SDT) to gather attentional data and to measure change in performance after AMPH administration. To quantify DAT in striatum, this project used immunohistochemistry to measure fluorescent intensity, a measurement representing the corresponding DAT expression level. To support findings, the project used drift diffusion models (DDMs) to enhance our understanding of the decision-making process affected by DAT expression. Results: The results showed non-significant correlations between DAT density in DS and VS and premature responses and accuracy in the SDT. The data also showed that the DAT DS:VS expression ratio significantly correlates to AMPH sensitivity. Conclusion: We concluded that increased DAT expression in striatum can predict worse accuracy and increased premature response times during a SDT. The project also found that the ratio of DAT expression in dorsal and ventral striatum can predict changes in accuracy after AMPH administration.

Background: In health status assessment, patient-reported outcome (PRO) measures are tools used to elicit important and measurable information from patients to better understand the impact of health conditions on their lives. Such impacts are considered latent constructs, or variables that cannot be observed or measured directly. Instruments intended to assess latent constructs must satisfy certain development, psychometric, and scaling standards through the generation of both qualitative and quantitative evidence to demonstrate the adequacy of its measurement properties. Health-related quality of life (HRQOL), or the subjective perception of health, is a core concept within the field of PROs. The Short Form 36 (SF-36) is one of the most commonly used PROs used to assess health-related quality of life (HRQOL).Objectives: To provide a better understanding of the performance and dimensionality of the SF-36 version 2 in a cross-sectional sample of patients with multiple sclerosis (MS) on an item, subscale, and higher-order factor structure level using different measurement methods grounded in classical test theory (CTT), factor analysis, and item response theory (IRT).Methods: This was a post hoc analysis of a cross-sectional dataset. Patients with MS were recruited to participate in an online survey asking a variety of questions related to their health and treatment seeking behaviors. The SF-36 was one of the questionnaires included in the survey. Items and individual subscales were evaluated using a multi-trait/multi-item correlation matrix to assess item-to-subscale relationships, including item discriminant validity with other subscales. Unidimensionality for select SF-36 subscales was assessed through confirmatory factor analysis (CFA). Internal consistency reliability (Cronbach's alpha) was evaluated for each subscale. Patient-reported disability, depression, and current symptom exacerbation status were evaluated relative to SF-36 subscale scores to assess convergent validity, discriminant validity, and known-groups validity. Higher-order factor models of the SF-36 were tested to evaluate dimensionality of the instrument, including a two-factor second-order factor model, a bifactor model, and a statistical comparison between the bifactor model and its corresponding nested model. Unidimensionality was further evaluated through the use of graded response IRT models. The relative fit of traditional versus discrimination-constrained models was tested using a -2 loglikelihood ratio test, followed by an evaluation of item-level properties for fit (S-X² statistics), local dependence, and further assessment of model parameters (discrimination parameters, location parameters, option response functions, and test information curves). Person location parameters were also estimated to compare scale information to the location of patients along the latent construct. Results: A total of 1,052 respondents completed the survey. Unidimensionality of individual subscales evaluated via CFA all had confirmatory fit indices (CFI)>0.90, butroot mean square error of approximation [RMSEA] values all exceeded 0.08. All IRT graded response models showed a statistically significant improvement in model fit when item discrimination was freely estimated. Each subscale from the IRT models had at least one mis-fitting item across all unidimensional scales tested (S-X² p-value>0.05), and nearly all subscales tested showed item pairs with signs of local dependence. Cronbach's alpha was>0.80 for all subscales except for General Health [GH] (alpha = 0.78). SF-36 subscales most closely related to physical aspects of health status had the strongest relationship to disability status (physical functioning [PF], r = -0.82, and role physical [RP], r = -0.57). Subscales more closely related to mental health had the largest effect sizes between patients with versus without depression (0.88 for mental health [MH] subscale) and the smallest effect sizes between patients reporting currently experiencing versus not experiencing an exacerbation of their symptoms (0.48 for role emotional [RE]subscale). Both CFA and IRT analyses showed lack of compelling evidence supporting unidimensionality upon combining items from the PF, RP, bodily pain [BP], and GH subscales to form the Physical-21, and upon combining items from the VT, role emotional (RE), social functioning (SF), and MH subscales to form the Mental-14. Higher-order factor models showed good model fit, with CFI>0.90 in all cases and lower RMSEA values than seen for the individual subscales (0.077 to 0.107). The bifactor model fit significantly better than its nested second-order version, however, the best-fitting (i.e., highest CFI and lowest RMSEA) higher-order factor model was the preliminary first-order model with eight first-order factors consistent with the eight subscales of the SF-36 (CFI=0.996, RMSEA=0.077, X² = 3872.14, p<0.001). Conclusions: The SF-36 version 2 performed well when evaluated within the CTT framework, but both CFA and IRT methods revealed several limitations at the item and factor level across all subscales, due to item wording (i.e., positive versus negative), items not being sufficiently related to its latent construct, and local dependence of items within and across subscales. The appropriateness of equal weighting of responses to produce a single summary score for each subscale, as well as their further aggregation into the Physical Component Summary and Mental Component Summary scores should be reevaluated.

Background and Objectives: Integrase strand transfer inhibitors (INSTIs), dolutegravir, elvitegravir, and raltegravir, have become integral in the treatment of HIV, with close monitoring of continued efficacy and tolerability. As side effect occurrence varies among subjects receiving these drugs, we sought to perform an exploratory analysis examining the role of several single-nucleotide polymorphisms (SNPs) on drug concentration changes, selected clinical outcomes, and the occurrence of subject-reported adverse events. Methods: Adults (aged ≥ 18 years) receiving INSTI-based regimens for treatment of HIV were recruited and genotyped with an iPLEX ADME PGx Pro v1.0 Panel. Multiple linear or logistic regression with covariates [age, sex, BMI, regimen (in the across-regimen group), regimen duration, and baseline variables (for continuous parameters)] was used to detect significant association of selected clinical data with genetic variants within the study population. Results: In a sample with a median age of 52.5 years (IQR 45.7–57.2) being predominately Caucasian (88.6%) and male (86.4%), this exploratory study discovered several associations between variables and SNPs, when using INSTIs. Abnormal dream occurrence was statistically different between regimens. Additionally, several SNPs were found to be associated with adverse event profiles primarily when all regimens were grouped together. Conclusions: The associations found in this study point to a need for further assessment, within the population living with HIV, of factors contributing to unfavorable subject outcomes. These exploratory findings require confirmation in larger studies, which then may investigate pharmacogenetic mechanisms.

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Purpose: Three different generic vancomycin products were compared using liquid chromatography–mass spectrometry (LC-MS) and open-access metabolomic tools. Methods: Single-lot samples of vancomycin hydrochloride from three different manufacturers (Hospira, APP Pharmaceuticals, and Pfizer) were reconstituted and injected into a high-resolution LC-MS system. The mass spectral fingerprints were compared for similarity of nonvancomycin B components using the XCMS Online system through Scripps University. Significance was defined as a p of ≤0.01 and a fold change of ≥1.5. The concentration of vancomycin B in each product was also measured using LC-MS on days 0, 1, 2, 4, 7, 10, and 14. Results: Qualitative comparisons of the products using the XCMS Online interface indicated the presence of significant differences among the products at the time of reconstitution; however, these variations seemed to converge after 14 days of storage. The concentration profiles of vancomycin B during refrigerated storage did not differ significantly among the three products. XCMS Online analyses revealed that the Pfizer and Hospira products were the most similar to each other. Conclusion:While there were no significant differences found in the concentration of vancomycin B among Pfizer, APP, and Hospira products, there were differences in their initial mass spectral analysis after reconstitution. Liquid chromatography–tandem mass spectrometry profiles of the ions or isotopes present in the three products showed significant differences in impurities such as crystalline degradation product (CDP)-1 and CDP intermediate. After 14 days of refrigerated storage, the differences among the products converged, and fewer distinct features could be detected.

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The purpose of this study was to quantify the amounts of the d- and l-threo enantiomers of methylphenidate in maternal plasma, placenta, and maternal and fetal brain tissue following prenatal exposure and to establish a pharmacokinetic profile for MPH during pregnancy. Due to increasing rates of use of methylphenidate amongst females of childbearing age, it is important to understand the extent of exposure to the fetus. Briefly, pregnant mice were injected with 5 mg/kg methylphenidate at 18 days gestation, and tissue was collected 1, 5, 10, 30, 60, and 120 min following injection. Methylphenidate was extracted from tissue via solid phase extraction, and concentrations were determined using liquid chromatography–mass spectrometry (LC–MS). Because methylphenidate is administered as a racemic mixture of d- and l-threo enantiomers and the d-enantiomer is more pharmacologically active, the enantiomers were quantified separately. Interestingly, we found that methylphenidate does cross the placenta and enter the fetal brain. Although the highest concentrations were achieved in maternal brain, the concentrations of d- and l-methylphenidate in fetal brain were comparable to those of maternal plasma. Additionally, both d- and l-methylphenidate had longer half-lives in placenta than in maternal or fetal brain. Interestingly, there was a bimodal peak in maternal brain concentrations, at 5 min and again at 60 min, which was not observed in maternal plasma. Finally, the total exposure (as represented by area under the curve) was statistically significantly higher for the active d-enantiomer than the l-enantiomer in maternal brain tissue. In conclusion, methylphenidate crosses the placenta and reaches measurable concentrations in fetal brain. Although long-term behavioral and developmental studies are needed to determine specific outcomes of prenatal exposure, discussion with pregnant patients on the potential risks of methylphenidate exposure is warranted.