Interindividual variability, influenced by patient-specific factors including age, weight, gender, race, and genetics, among others, contributes to variations in therapeutic response. Population pharmacokinetic (popPK) modeling is an essential tool for pinpointing measurable factors affecting dose-concentration relationships and tailoring dosage regimens to individual patients. Herein, we developed a popPK model for salbutamol, a short-acting β2-agonist (SABA) used in asthma treatment, to identify key patient characteristics that influence treatment response. To do so, synthetic data from physiologically-based pharmacokinetic (PBPK) models was employed, followed by an external validation using real patient data derived from an equivalent study. Thirty-two virtual patients were included in this study. A two-compartment model, with first-order absorption (no delay), and linear elimination best fitted our data, according to diagnostic plots and selection criteria. External validation demonstrated a strong agreement between individual predicted and observed values. The incorporation of covariates into the basic structural model identified a significant impact of age on clearance (Cl) and intercompartmental clearance (Q); gender on Cl and the constant rate of absorption (ka); race on Cl; and weight on Cl in the volume of distribution of the peripheral compartment (V2). This study addresses critical challenges in popPK modeling, particularly data scarcity, incompleteness, and homogeneity, in traditional clinical trials, by leveraging synthetic data from PBPK modeling. Significant associations between individual characteristics and salbutamol\'s PK parameters, here uncovered, highlight the importance of personalized therapeutic regimens for optimal treatment outcomes.

BACKGROUND: Decades of research have firmly established that cognitive health and cognitive treatment services are a key need for people living with psychosis. However, many current clinical programs do not address this need, despite the essential role that an individual\'s cognitive and social cognitive capacities play in determining their real-world functioning. Preliminary practice-based research in the Early Psychosis Intervention Network early psychosis intervention network shows that it is possible to develop and implement tools that delineate an individuals\' cognitive health profile and that help engage the client and the clinician in shared decision-making and treatment planning that includes cognitive treatments. These findings signify a promising shift toward personalized cognitive health.
METHODS: Extending upon this early progress, we review the concept of interindividual variability in cognitive domains/processes in psychosis as the basis for offering personalized treatment plans. We present evidence from studies that have used traditional neuropsychological measures as well as findings from emerging computational studies that leverage trial-by-trial behavior data to illuminate the different latent strategies that individuals employ.
UNASSIGNED: We posit that these computational techniques, when combined with traditional cognitive assessments, can enrich our understanding of individual differences in treatment needs, which in turn can guide evermore personalized interventions.
CONCLUSIONS: As we find clinically relevant ways to decompose maladaptive behaviors into separate latent cognitive elements captured by model parameters, the ultimate goal is to develop and implement approaches that empower clients and their clinical providers to leverage individual\'s existing learning capacities to improve their cognitive health and well-being.

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Temporal interference stimulation (TIS) uses two pairs of conventional transcranial alternating current stimulation (tACS) electrodes, each with a different frequency, to generate a time-varying electric field (EF) envelope (EFE). The EFE focality in primary somatosensory and motor cortex areas of a standard human brain was computed using newly defined linear alignment montages. Sixty head volume conductor models constructed from magnetic resonance images were considered to evaluate interindividual variability. Six TIS and two tACS electrode montages were considered, including linear and rectangular alignments. EFEs were computed using the scalar-potential finite-difference method. The computed EFE was projected onto the standard brain space for each montage. Computational results showed that TIS and tACS generated different EFE and EF distributions in postcentral and precentral gyri regions. For TIS, the EFE amplitude in the target areas had lower variability than the EF strength of tACS. However, bipolar tACS montages showed higher focality in the superficial postcentral and precentral gyri regions than in TIS. TIS generated greater EFE penetration than bipolar tACS at depths <5-10 mm below the brain surface. From group-level analysis, tACS with a bipolar montage was preferred for targets <5-10 mm in depth (gyral crowns) and TIS for deeper targets. TIS with a linear alignment montage could be an effective method for deep structures and sulcal walls. These findings provide valuable insights into the choice of TIS and tACS for stimulating specific brain regions.

Arylacetamide deacetylase (AADAC) is involved in drug hydrolysis and lipid metabolism. In 23 human liver samples, no significant correlation was observed between AADAC mRNA (19.7-fold variation) and protein levels (137.6-fold variation), suggesting a significant contribution of post-transcriptional regulation to AADAC expression. The present study investigated whether AADAC is regulated by microRNA in the human liver and elucidate its biological significance. Computational analysis predicted two potential miR-222-3p recognition elements in the 3\'-untranslated region (UTR) of AADAC. Luciferase assay revealed that the miR-222-3p recognition element was functional in downregulating AADAC expression. In HEK293 cells transfected with an AADAC expression plasmid containing 3\'-UTR, miR-222-3p overexpression decreased AADAC protein level and activity, whereas miR-222-3p inhibition increased them. Similar results were observed in human hepatoma-derived Huh-1 cells endogenously expressing AADAC and HepaSH cells that are hepatocytes from chimeric mice with humanized livers. In individual human liver samples, AADAC protein levels inversely correlated with miR-222-3p levels. Overexpression of miR-222-3p resulted in increased lipid accumulation in Huh-1 cells, which was reversed by AADAC overexpression. In contrast, miR-222-3p inhibition decreased lipid accumulation, which was reversed by AADAC knockdown. In conclusion, we found that hepatic AADAC was downregulated by miR-222-3p, resulting in decreased drug hydrolysis and increased lipid accumulation.

Flavonoids exert vasculoprotective effects in humans, but interindividual variability in their action has also been reported. This study aims to identify genes that are associated with vascular health effects of flavonoids and whose polymorphisms could explain interindividual variability in response to their intake. Applying the predetermined literature search criteria, we identified five human intervention studies reporting positive effects of flavonoids on vascular function together with global genomic changes analyzed using microarray methods. Genes involved in vascular dysfunction were identified from genome-wide association studies (GWAS). By extracting data from the eligible human intervention studies, we obtained 5807 differentially expressed genes (DEGs). The number of identified upstream regulators (URs) varied across the studies, from 227 to 1407. The search of the GWAS Catalog revealed 493 genes associated with vascular dysfunction. An integrative analysis of transcriptomic data with GWAS genes identified 106 candidate DEGs and 42 candidate URs, while subsequent functional analyses and a search of the literature identified 20 top priority candidate genes: ALDH2, APOE, CAPZA1, CYP11B2, GNA13, IL6, IRF5, LDLR, LPL, LSP1, MKNK1, MMP3, MTHFR, MYO6, NCR3, PPARG, SARM1, TCF20, TCF7L2, and TNF. In conclusion, this integrated analysis identifies important genes to design future nutrigenetic studies for development of precision nutrition for polyphenols.

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Trace elements and heavy metals play pivotal roles in health status by regulating a myriad of vital biological functions. Abnormal metal homeostasis has been linked to a constellation of pathogenic complications, including oxidative stress, inflammatory processes, dyslipidemia, and impaired insulin-mediated metabolism of carbohydrates, thereby increasing the odds of developing childhood obesity and related comorbidities. Herein, we provide a comprehensive revision of recent literature on the association between childhood obesity, trace elements, and heavy metals. Further, we emphasize on the crucial importance of addressing the influence that interindividual variability factors (e.g., sex, age, genetic determinants, concomitance of comorbidities, and environmental factors) may have in modulating the susceptibility to disease development. Altogether, this review article represents a concise guide to better understand the involvement of metals in childhood obesity pathogenesis and discusses future needs with the aim of establishing robust biomarkers in the context of precision medicine.

Using theta burst stimulation (TBS) to induce neural plasticity has played an important role in improving the treatment of neurological disorders. However, the variability of TBS-induced synaptic plasticity in the primary motor cortex prevents its clinical application. Thus, factors associated with this variability should be explored to enable the creation of a predictive model. Statistical approaches, such as regression analysis, have been used to predict the effects of TBS. Machine learning may potentially uncover previously unexplored predictive factors due to its increased capacity for capturing nonlinear changes. In this study, we used our prior dataset (Katagiri et al., 2020) to determine the factors that predict variability in TBS-induced synaptic plasticity in the lower limb motor cortex for both intermittent (iTBS) and continuous (cTBS) TBS using machine learning. Validation of the created model showed an area under the curve (AUC) of 0.85 and 0.69 and positive predictive values of 77.7 and 70.0% for iTBS and cTBS, respectively; the negative predictive value was 75.5% for both patterns. Additionally, the accuracy was 0.76 and 0.72, precision was 0.82 and 0.67, recall was 0.82 and 0.67, and F1 scores were 0.82 and 0.67 for iTBS and cTBS, respectively. The most important predictor of iTBS was the motor evoked potential amplitude, whereas it was the intracortical facilitation for cTBS. Our results provide additional insights into the prediction of the effects of TBS variability according to baseline neurophysiological factors.

Many oncology drugs have been found to induce cardiotoxicity in a subset of patients, which significantly limits their clinical use and impedes the benefit of lifesaving anticancer treatments. Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) carry donor-specific genetic information and have been proposed for exploring the interindividual difference in oncology drug-induced cardiotoxicity. Herein, we evaluated the inter- and intraindividual variability of iPSC-CM-related assays and presented a proof of concept to prospectively predict doxorubicin (DOX)-induced cardiotoxicity (DIC) using donor-specific iPSC-CMs. Our findings demonstrated that donor-specific iPSC-CMs exhibited greater line-to-line variability than the intraindividual variability in impedance cytotoxicity and transcriptome assays. The variable and dose-dependent cytotoxic responses of iPSC-CMs resembled those observed in clinical practice and largely replicated the reported mechanisms. By categorizing iPSC-CMs into resistant and sensitive cell lines based on their time- and concentration-related phenotypic responses to DOX, we found that the sensitivity of donor-specific iPSC-CMs to DOX may predict in vivo DIC risk. Furthermore, we identified a differentially expressed gene, DND microRNA-mediated repression inhibitor 1 (DND1), between the DOX-resistant and DOX-sensitive iPSC-CMs. Our results support the utilization of donor-specific iPSC-CMs in assessing interindividual differences in DIC. Further studies will encompass a large panel of donor-specific iPSC-CMs to identify potential novel molecular and genetic biomarkers for predicting DOX and other oncology drug-induced cardiotoxicity.