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Results for “Predictive modeling”

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PubMed1992

Mink as a predictive model in toxicology.

This paper reviewed the biomedical and toxicological database concerning the use of mink as a predictive model of human responses. It is concluded that substantial information exists on the mink genetics, physiology, metabolism, nutritional requirements, and susceptibility to infectious disease; and provides a foundation upon which interspecies extrapolation may be considered. In addition, information on the response of mink to several dozen toxic substances revealed that mink respond in a qualitatively and quantitatively similar manner to other more commonly employed species as well as humans. Our conclusion does not infer that mink should be used routinely in toxicological testing for estimation of human responses. However, it indicates that toxicological data from this species may be a useful complement in risk assessment processes based upon data obtained from traditionally employed models such as rats and dogs.

Animals↗

PubMed2026

Netupitant versus aprepitant: model-predicted neurokinin-1 receptor occupancy and implications for long-delayed nausea and vomiting prevention.

PURPOSE: Nausea and vomiting beyond 5 days after emetogenic chemotherapy or antibody-drug conjugate (ADC) therapy are common, yet the role of neurokinin-1 (NK1) receptor antagonists in this setting remains underrecognized. We used pharmacokinetic/pharmacodynamic (PK/PD) modeling to estimate the NK1 receptor occupancy (RO), a proxy for clinical efficacy, for up to 20 days after a single 300 mg dose of oral netupitant, 3-day oral aprepitant (125 mg on day 1; 80 mg on days 2-3), or a single 165 mg dose of oral aprepitant. METHODS: Data from previous PK studies were analyzed by compartmental modeling. Positron emission tomography studies assessing striatal NK1 RO were used to develop maximum drug effect PD models, which were fitted to NK1 RO data as a function of plasma concentrations. RESULTS: Model predicted NK1 RO exceeded 90% at 3 h for all treatments. Thereafter, RO declined more gradually with netupitant (76%, 70%, 60%, and 21% on days 5, 7, 10, and 20, respectively) than with 3-day aprepitant (81%, 39%, 3%, and negligible) or single-dose aprepitant (45%, 10%, < 1%, and negligible). The half-life of netupitant was ~ 6.1 times longer than aprepitant's. Netupitant plasma concentration remained above the effective concentration for 50% NK1 RO (EC50) through day 10, whereas aprepitant concentrations fell below the EC50 by ~ days 7 and 5 after repeated and single dosing, respectively. CONCLUSIONS: Single-dose netupitant maintained NK1 RO substantially longer than repeated- and single-dose aprepitant, suggesting greater potential for prolonged prevention of nausea and vomiting in ADC-treated patients. Prospective clinical validation of these model predictions would be beneficial.

Aprepitant↗

PubMed1991

[Chronic lymphatic leukemia. II. Analysis of prognostic factors and development of survival predicting models. Study of 187 patients].

The univariate analysis of prognostic factors showed the value of clinical variables (age, symptoms, lymph node enlargement, splenomegaly, hepatomegaly, clinical stage), haematological variables (haemoglobin, platelet count, leucocyte count, lymphocyte count, bone marrow involvement and biopsy pattern, cleaved lymphocytes, prolymphocytes, LDT) and biochemical variables (BUN, creatinine, calcium, phosphorus, uric acid and albumin). The multivariate analysis chose the combination of Rai's stage, age, cervical lymph node involvement, phosphorus and BUN. Two predictive models capable of separating appropriately low, intermediate and high risk groups were developed and validated. The results were compared with others in the literature. The interest of predictive models arising from multivariate analysis is stressed.

Adult↗

PubMed1987

Development and application of a prediction model for dental caries.

The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk.

Child↗

PubMed1989

Usefulness of a single-dose prediction model for the determination of long-term maintenance therapy of valproic acid.

The single-dose prediction model (SDPM) of Slattery et al. has been shown accurately to predict short-term (3 to 7 days) steady-state valproic acid (VPA) concentrations in normal volunteers and in hospitalized patients receiving monotherapy. To assess the long-term usefulness of the SDPM in a real-life clinic setting, six ambulatory patients ranging in age from 2 to 8 years were studied. Blood samples were drawn at least 6 h after the initial dose but before the second dose of VPA, A steady-state trough concentration (Cminss) was measured 3 to 7 days after the initiation of therapy. Dosage adjustments and alterations in therapeutic regimen were allowed and another Cminss was measured after 1 to 12 months. Predicted Cminss values were calculated for both the short-term and long-term VPA regimens. Predictive performance analysis demonstrated that the SDPM was unbiased in predicting both short-term and long-term VPA Cminss values and precise in predicting only short-term VPA Cminss values. The SDPM is not a reliable predictor of long-term total VPA concentrations in seizure patients in an outpatient clinic setting.

Child↗

PubMed1990

Validity of knee flexion and extension peak torque prediction models.

The primary purpose of this study was to test the validity of predictive models relating isokinetic knee torque production to anthropometric and demographic variables. Subjects were 23 healthy female and 15 healthy male volunteers between the ages of 10 and 77 years. We measured subjects' peak knee flexion and extension torque production at two angular velocities. For each torque dependent variable, we calculated a Pearson product-moment correlation coefficient between the measured torque values and the values obtained with prediction equations. The difference between the squared value of the correlation coefficients and the regression multiple R2 values obtained for an original group of 134 subjects ranged between .05 and .10 for the torque dependent variables. The results indicate the validity of the regression models at the level specified by the multiple regression R2 values. Clinicians can use the prediction equations presented in this article to establish rehabilitation goals for patients and can estimate the error involved in applying each prediction equation.

Adolescent↗

PubMed2026

Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.

BACKGROUND: Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples. METHODS: Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration. RESULTS: Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples. CONCLUSIONS: This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for blood-based CRC detection.

Colorectal cancer (CRC)↗

PubMed1971

Prediction models for 90Sr in shed deciduous teeth and infant bone.

Shed deciduous teeth were collected in 1966-69 in Denmark, the Faroes and Greenland from children born in the period 1953-63. 235 samples of crowns were analysed for 90Sr. The 90Sr levels in deciduous tooth crowns were related to the fall-out rate and the accumulated fall-out. The tooth levels in children born in 1950-62 could be described with the same equation as the 90Sr bone levels in 1-yr-old infants born in 1962-68. The prediction models for 90Sr in teeth and bones showed that for given amount of fall-out the Faroese levels became nearly twice as high as the Danish. The maximum teeth and bone levels were found in children born in 1963, where the Faroese level was estimated from the prediction model to be 24 pCi 90Sr/g Ca.

Bone and Bones↗

PubMed1987

Keeping juvenile delinquents in school: a prediction model.

The purpose of this study was to test an empirically based prediction model of school dropout on a sample of 137 juvenile delinquents, some who have dropped out and some who have remained in school. The specific factors among the many found in previous research that are salient for predicting whether delinquent youths will drop out or remain in school were determined. An important finding of this study is that it required only four factors to yield a high level of prediction: misbehavior in school, disliking school, the negative influence of peers with respect to dropping out and getting into trouble, and a marginal or weak relationship with parents. The four factors identified create a model that is directly applicable to prevention strategies and is extremely parsimonious.

Adolescent↗

PubMed2026

ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.

MOTIVATION: Chemical toxicity assessment is critical for drug development and environmental safety. Computational models have emerged as a promising alternative to animal testing and now play a significant role in efficiently evaluating new chemicals. To address the urgent need for user-friendly machine learning tools in computational toxicology, we developed ToxiVerse, a public web-based platform. RESULTS: ToxiVerse provides automatic chemical bioprofiling, curated toxicity datasets, and a predictive modeling interface designed for researchers who lack programming expertise. The platform comprises three integrated modules: (i) Bioprofiler, which provides chemical descriptors by combining chemical-bioactivity data from PubChem assays with a machine learning-based data gap-filling procedure; (ii) Database, which hosts ∼50 000 curated chemicals covering diverse toxicity endpoints; and (iii) Cheminformatics, which enables dataset upload, chemical curation, and automatic generation of quantitative structure-activity relationship models for toxicity prediction. AVAILABILITY: The tool is accessible at www.toxiverse.com, and source code is available at https://github.com/zhu-research-group/toxiverse.

Quantitative Structure-Activity Relationship↗

PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗

PubMed1992

A comparative investigation of hepatic clearance models: predictions of metabolite formation and elimination.

Liver clearance models serve to improve our understanding of the relationships between the physiological determinants and hepatic clearance and predict changes in the disposition of substrates when homeostasis of the organ is perturbed. Their ability to describe metabolism was presently extended to the sequential formation and elimination of primary (M1), secondary (M2), and tertiary (M3) metabolites during a single passage of drug (P) across the liver, under steady state and first-order conditions. The well-stirred model is distinct from other models in that metabolite formation and elimination is independent of enzymic distributions, the number of steps involved in metabolite formation, and the intrinsic clearances of the precursors. This model predicts that the extraction ratio of a formed primary metabolite derived from drug (E[M1, P]) is identical to that for the preformed primary metabolite (E[M1]), and that the extraction ratios of a secondary metabolite derived from drug (E[M2, P]) and primary metabolite (E[M2, M1]) or preformed secondary metabolite (E[M2]) are identical. For the more physiologically acceptable, parallel-tube and dispersion models, metabolite sequential elimination is highly influenced by the intrinsic clearances of the precursors and the enzymic distributions that mediate removal of precursor species and the metabolites. Furthermore, the extent of sequential metabolism recedes as the number of steps involved for metabolite formation increases. These models predict that E[M1, P] less than E[M1], and E[M2, P] less than E[M2, M1] less than E[M2], with the magnitude of the changes being less for the dispersion model than for the parallel-tube model. Competing pathways that divert substrate from entering the sequential pathway were found to exert only minimal influence on the sequential pathway.

Liver↗

PubMed1991

Mathematical analysis of perifusion data: models predicting elution concentration.

System models are constructed and analyzed for combined convective flow and for dispersion in distorting concentrations of a chemical signal as it passes through a packed column. We derive general analytical solutions for these models. The results have applications to analyses such as in biological experiments involving hormonal stimulation of perifused cells, elution chromatography, adsorption columns, and studies of groundwater flow. The models reveal that the column distorts an incoming signal (such as a change in solute concentration in the flowing liquid) at the inlet. This distortion is greatest at low values of the Peclet number of the flow and is small at larger values. We explore the effects of the approximations inherent in the mathematical models of the system. Specification of the boundary conditions of the problem are shown to be particularly important. With the use of incorrect models, it is possible to obtain accurate interpolations to data obtained from perfusion experiments. However, the parameters derived (in particular the dispersion constant and the peak concentration of a solute concentration pulse) may be considerably in error. This may lead to errors when these parameter estimates are used to predict results in other experimental situations.

Animals↗

PubMed2025

A prediction model for metachronous colorectal cancer: development and validation.

BACKGROUND: Being able to estimate the risk of metachronous disease in a patient with colorectal cancer (CRC) could enable risk-appropriate surveillance. The aim of this study was to develop a risk-prediction model to estimate individual 10-year risk of metachronous disease following a CRC diagnosis. METHODS: A population-based cohort of patients with CRC was recruited soon after diagnosis between 1997 and 2012 from the United States, Canada, and Australia. Cox regression with the least absolute shrinkage and selection operator penalization was used to identify factors that predicted the risk of a new primary CRC diagnosed at least 1 year after the initial CRC diagnosis. Potential predictors included demography, anthropometry, lifestyle factors, comorbidities, personal and family cancer history, medication use, age at diagnosis, and pathological features of the first CRC. Internal validation through bootstrapping was used to evaluate the discrimination and calibration. RESULTS: We included 6085 CRC cases; 138 (2.3%) of these cases were diagnosed with metachronous disease over a median of 12 years (IQR = 5-17 years). Metachronous CRC risk was predicted by body mass index; smoking status; level of physical activity; family history of cancer and synchronous CRC; stage, grade, histological type, and DNA mismatch repair status; and age at diagnosis of the first CRC. The model was valid with a C statistic of 0.65 (95% CI = 0.63 to 0.68) and a calibration slope of 0.873 (SD = 0.087). CONCLUSIONS: Metachronous CRC can be predicted with reasonable accuracy using a prediction model that consists of clinical variables collected as part of routine practice.

Humans↗

PubMed1989

Individual outcome prediction models for intensive care units.

Prognostic criteria based on static analysis of group statistics do not help much in decisions to withhold or withdraw therapy from intensive care unit (ICU) patients too ill to benefit, since they do not provide adequate information on the features that distinguish non-survivors from survivors. A predictive model which uses dynamic analysis of severity scores based on physiological variables is presented here along with the results of tests of the model in 831 ICU patients. 109 patients were correctly predicted to die by the model. Of 722 whose prediction was outcome unknown, 181 died. Thus, the odds for prediction of death among non-survivors were 0.376. Since there were no false predictions of death, the estimated chance of a false prediction was 0.0055.

Bayes Theorem↗

PubMed1992

Structure and parameterization of pharmacokinetic models: their impact on model predictions.

There has been an increasing interest in physiologically based pharmacokinetic (PBPK) models in the area of risk assessment. The use of these models raises two important issues: (1) How good are PBPK models for predicting experimental kinetic data? (2) How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five-compartment PBPK model, a three-compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1) the difference is larger between the predictions of the same model fitted to different data sets than between the predictions of different models fitted to the dame data; and (2) the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.

Animals↗

PubMed1991

A predictive model for DNA recognition by the herpes simplex virus protein ICP4.

The herpes simplex virus (HSV) type 1 immediate early protein ICP4 is an essential regulatory enzyme that binds DNA directly in order to stimulate or repress gene expression. The degree of transaction is related to the locations and affinities of the ICP4 binding sites. A number of binding sites have been identified; some sites showed obvious homology to one another, and these were called consensus ICP4 binding sites. Other binding sites did not appear to be related, and these were termed non-consensus sites. We hypothesized, however, that a single model could describe all ICP4 binding sites, given the appropriate characterizations of sites. We performed statistical analyses on a set of ICP4 binding sites and found that the bases important for defining binding were located within a 13 base region. Missing contact analyses on several high-affinity binding sites revealed the same 13 base region as important for critical protein-DNA contacts. From these data we derived the consensus sequence RTCGTCNNYNYSG, where R is purine, Y is pyrimidine, S is C or G, and N is any base. In addition, we found that a better profile for ICP4 binding sites involves use of a matrix of base proportions from the binding site data; sites are analyzed by calculating the Matrix Mean score. We show that this Matrix Mean model could accurately predict the locations of novel ICP4 binding sites. Finally, we analyzed the entire HSV-1 genome for potential ICP4 binding sites and speculate about what these results suggest for the role of ICP4 in viral gene regulation.

Amino Acid Sequence↗

PubMed1991

Effects of cannabidiol in animal models predictive of antipsychotic activity.

The effects of cannabidiol (CBD) were compared to those produced by haloperidol in rats submitted to experimental models predictive of antipsychotic activity. Several doses of CBD (15-480 mg/kg) and haloperidol (0.062-1.0 mg/kg) were tested in each model. First, CBD increased the effective doses 50% (or) ED50 of apomorphine for induction of the sniffing and biting stereotyped behaviors. In addition, both CBD and haloperidol reduced the occurrence of stereotyped biting induced by apomorphine (6.4 mg/kg), increased plasma prolactin levels and produced palpebral ptosis, as compared to control solutions. However, CBD did not induce catalepsy even at the highest doses, in contrast to haloperidol. Such a pharmacological profile is compatible with that of an "atypical" antipsychotic agent, though the mechanism of action is uncertain and may not be identical to that of the dopamine antagonists.

Animals↗