Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure–activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals

Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure–activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals

Hypothesized mediating effects of on the relationships between impairments of CRs, social support and caregiver mental health.

<p>Path a represents the influence of predictors (impairments of care recipients and caregiver social support) on the mediators (five domains of caregiver self-efficacy). Path b represents the influence of meidators on outcome measure (caregiver mental health). Path c represents the direct effects of predictors on outcome measure, and Path c’ demonstrates the predictors indirectly influence outcome measure via the influence of the mediators.</p

Descriptive statistics for MMSE, DAD, RMBPC, MOS-SSS, caregiver self-efficacy and mental health.

<p><b>Abbreviations:</b> MMSE, Mini Mental Status Examination; DAD-ADLs, Activity of Daily Living Subscale of Disability Assessment in Dementia; DAD-ADLs, Instrumental Activities of Daily Living Subscale of Disability Assessment in Dementia; RMBPC, Revised Memory and Behaviour Problems Checklist; MOS-SSS, Medical Outcome Study Social Support Survey; SEQCFC, Self-Efficacy Questionnaire for Chinese Family Caregivers; MCS, Mental Component Summary score (MCS) of the Medical Outcome Study (MOS) Short-Form (SF-36) Health Survey.</p

Regressions of caregiver self-efficacy on caregiver mental health (path b).

<p><b>Abbreviations:</b> MCS, Mental Component Summary score (MCS) of the Medical Outcome Study (MOS) Short-Form (SF-36) Health Survey; GI, Self-Efficacy for Gathering Information about Treatment, Symptoms and Health Care; OS, Self-Efficacy for Obtaining Support; RBD, Self-Efficacy for Responding to Behavior Disturbances; MHPMC, Self-Efficacy for Managing Household, Personal and Medical Care; MDC, Self-Efficacy for Managing Distress Associated with Caregiving.</p>*<p>P≤.05; **P≤.01; ***P≤.001.</p

Partial mediating effect of managing caregiving distress self-efficacy on the relationship between positive social interaction and caregiver mental health.

<p>Partial mediating effect of managing caregiving distress self-efficacy on the relationship between positive social interaction and caregiver mental health.</p

Partial mediating effect of managing caregiving distress self-efficacy on the relationship between BPSD and caregiver mental health.

<p>Partial mediating effect of managing caregiving distress self-efficacy on the relationship between BPSD and caregiver mental health.</p

Partial mediating effect of gathering information self-efficacy on the relationship between positive social interaction and caregiver mental health.

<p>Partial mediating effect of gathering information self-efficacy on the relationship between positive social interaction and caregiver mental health.</p

Regressions of dementia-related impairments, social support on caregiver mental health (path c).

<p><b>Abbreviations:</b> MCS, Mental Component Summary score (MCS) of the Medical Outcome Study (MOS) Short-Form (SF-36) Health Survey; DAD-ADLs, Activity of Daily Living Subscale of Disability Assessment in Dementia; DAD-ADLs, Instrumental Activities of Daily Living Subscale of Disability Assessment in Dementia; RMBPC, Revised Memory and Behaviour Problems Checklist; MOS-SSS, Medical Outcome Study Social Support Survey.</p>*<p>P≤.05; **P≤.01; ***P≤.001.</p

Virtual Cursor Task

Two-dimensional virtual cursor control task