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PubMed2001

Statistical models to predict the need for postoperative intensive care and hospitalization in pediatric surgical patients.

OBJECTIVE: To develop statistical models for predicting postoperative hospital and ICU stay in pediatric surgical patients based on preoperative clinical characteristics and operative factors related to the degree of surgical stress. We hypothesized that preoperative and operative factors will predict the need for ICU admission and may be used to forecast the length of ICU stay or postoperative hospital stay. DESIGN: Prospective data collection from 1,763 patients. SETTING: Tertiary care children's hospital. PATIENTS AND PARTICIPANTS: All pediatric surgical patients, including those undergoing day surgery. Patients undergoing dental or ophthalmologic surgical procedures were excluded. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: A logistic regression model predicting ICU admission was developed from all patients. Poissonregression models were developed from 1,161 randomly selected patients and validated from the remaining 602 patients. The logistic regression model for ICU admission was highlypredictive (area under the receiver operating characteristics (ROC) curve = 0.981). In the data set used for development of Poisson regression models, significant correlations occurred between the observed and predicted ICU stay (Pearson r = 0.468, p < 0.0001, n = 131) and between the observed and predicted hospital stay for patients undergoing general (r = 0.695, p < 0.0001), orthopedic (r = 0.717, p < 0.0001), cardiothoracic (r = 0.746, p < 0.0001), urologic (r = 0.458, p < 0.0001), otorhinolaryngologic (r = 0.962, p < 0.0001), neurosurgical (r = 0.7084, p < 0.0001) and plastic surgical (r = 0.854, p < 0.0001) procedures. In the validation data set, correlations between predicted and observed hospital stay were significant for general (p < 0.0001), orthopedic (p < 0.0001), cardiothoracic (p = 0.0321) and urologic surgery (p = 0.0383). The Poisson models for length of ICU stay, otorhinolaryngology, neurosurgery or plastic surgery could not be validated because of small numbers of patients. CONCLUSIONS: Preoperative and operative factors may be used to develop statistical models predicting the need for ICU admission in pediatric surgical patients, and hospital stay following general surgical, orthopedic, cardiothoracic and urologic procedures. These statistical models need to be refined and validatedfurther, perhaps using data collection from multiple institutions.

Arkansas↗

PubMed1994

Methods of assessing risk for periodontitis and developing multifactorial models.

In assessing risk for disease, periodontitis can be thought to be more like some of our common medical conditions: certain people are at higher risk than others, and efforts at prevention and intervention involve a combination of personal behaviors and professional practices. This paper presents some principles of designing risk assessment studies. In addition, the choices that must be made in deciding what is high risk and the type of model to be constructed are presented along with implications of each alternative. Terms such as risk indicators, risk factors, risk predictors, risk models, and prediction models are presented and discussed. To illustrate some of the issues, findings from the Piedmont 65+ Dental Study (a longitudinal study of oral health in older adults) indicate that: 1) indicators of risk developed from cross-sectional studies quite often are not confirmed as risk factors in longitudinal studies and longitudinal data implicate additional factors; 2) oral risk factors were important in explaining disease progression, but other categories of risk factors also played an explanatory role; 3) the risk models were able to predict who would experience attachment loss at some time during the 3-year period with a high degree of accuracy, but there was only moderate success at predicting who would not experience attachment loss; 4) including risk predictors in the models did improve the ability of one model to predict attachment loss, but the risk predictor masked the presence of an important risk factor; and 5) it is highly likely that adding measures of host defense mechanism to the study could result in improved models.

Aged↗

PubMed2004

A model to predict work-related fatigue based on hours of work.

Several research groups have developed models for estimating the work-related fatigue associated with shiftworkers' duty schedules. In June 2002, invited members of seven of these groups attended the Fatigue and Performance Modeling Workshop in Seattle, WA. At the workshop, each group described the background and conceptual basis of their model, and an independent party compared the models' predictions with performance and sleepiness data from five laboratory- and workplace-based scenarios. One of these models, the Fatigue Audit InterDyne (FAID), can be used to quantify the work-related fatigue associated with any duty schedule using hours of work (i.e., start/end times of work periods) as the sole input. The objectives of the current paper were to: 1) describe the background and conceptual basis of FAID; 2) present FAID-based predictions for four of the scenarios; and 3) discuss the advantages of, and possible improvements to, FAID. The analyses conducted to compare the predictive power of each model are described in detail by Van Dongen elsewhere in this issue.

Circadian Rhythm↗

PubMed2002

Pretransplant model to predict posttransplant survival in liver transplant patients.

OBJECTIVE: To develop a prognostic model that determines patient survival outcomes after orthotopic liver transplantation (OLT) using readily available pretransplant variables. SUMMARY BACKGROUND DATA: The current liver organ allocation system strongly favors organ distribution to critically ill recipients who exhibit poor survival outcomes following OLT. A severely limited organ resource, increasing waiting list deaths, and rising numbers of critically ill recipients mandate an organ allocation system that balances disease severity with survival outcomes. Such goals can be realized only through the development of prognostic models that predict survival following OLT. METHODS: Variables that may affect patient survival following OLT were analyzed in hepatitis C (HCV) recipients at the authors' center, since HCV is the most common indication for OLT. The resulting patient survival model was examined and refined in HCV and non-HCV patients in the United Network for Organ Sharing (UNOS) database. Kaplan-Meier methods, univariate comparisons, and multivariate Cox proportional hazard regression were employed for analyses. RESULTS: Variables identified by multivariate analysis as independent predictors for patient survival following primary transplantation of adult HCV recipients in the last 10 years at the authors' center were entered into a prognostic survival model to predict patient survival. Accordingly, mortality was predicted by 0.0293 (recipient age) + 1.085 (log10 recipient creatinine) + 0.289 (donor female gender) + 0.675 urgent UNOS - 1.612 (log10 recipient creatinine times urgent UNOS). The above variables, in addition to donor age, total bilirubin, prothrombin time (PT), retransplantation, and warm and cold ischemia times, were applied to the UNOS database. Of the 46,942 patients transplanted over the last 10 years, 25,772 patients had complete data sets. An eight-factor model that accurately predicted survival was derived. Accordingly, the mortality index posttransplantation = 0.0084 donor age + 0.019 recipient age + 0.816 log creatinine + 0.0044 warm ischemia (in minutes) + 0.659 (if second transplant) + 0.10 log bilirubin + 0.0087 PT + 0.01 cold ischemia (in hours). Thus, this model is applicable to first or second liver transplants. Patient survival rates based on model-predicted risk scores for death and observed posttransplant survival rates were similar. Additionally, the model accurately predicted survival outcomes for HCV and non-HCV patients. CONCLUSIONS: Posttransplant patient survival can be accurately predicted based on eight straightforward factors. The balanced application of a model for liver transplant survival estimate, in addition to disease severity, as estimated by the model for end-stage liver disease, would markedly improve survival outcomes and maximize patients' benefits following OLT.

Adult↗

PubMed1999

Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models.

MOTIVATION: The hidden Markov model (HMM) is a valuable technique for gene-finding, especially because its flexibility enables the inclusion of various sequence features. Recent programs for bacterial gene-finding include the information of ribosomal binding site (RBS) to improve the recognition accuracy of the start codon, using this feature. We report here our attempt to extend the model into the total transcriptional unit, enabling the prediction of operon structures. RESULTS: First, we improved the prediction accuracy of coding sequences (CDSs) by employing the models of 'typical', 'atypical' and 'negative (false-positive)' classes as well as the models of RBS and its downstream spacer. The sensitivity of exactly predicting the 204 experimentally confirmed CDSs reached 90.2% in an objective test. Based on the prediction result of CDSs, the positions of the promoters and terminators were predicted. Our model could exactly recognize 60% of 390 known transcriptional units. Thus, the accuracy and significance of this prediction problem is far from trivial. We would like to propose this problem as an open theme in bioinformatics because the ongoing or planned post-sequencing projects will produce much data for future improvements.

Algorithms↗

PubMed2004

Development and validation of experimental protocols for use of cardinal models for prediction of microorganism growth in food products.

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (aw) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food- and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10 degrees C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20 degrees C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, Sym'Previus, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.

Animals↗

PubMed1995

Predicting human heat strain and performance with application to space operations.

This Institute has developed a USARIEM Heat Strain Prediction Model for predicting physiological responses and soldier performance in the heat, which has been programmed for use by hand-held calculators and personal computers, and incorporated into the development of a heat strain decision aid. This model is demonstrated to predict accurately (generally within +/- 1 SD/SEM) rectal temperature (Tre) responses for soldiers wearing various military clothing ensembles during U.S. or non-U.S. military scenarios in the heat at home or abroad. The value of this model is shown presently for three NASA scenarios involving the Launch and Entry Suit (LES). The LES (ventilated or unventilated) is modeled during pre-launch/launch, re-entry/landing, and emergency egress after re-entry/landing scenarios, predominately to evaluate heat acclimation and hydration state effects. During the pre-launch/launch scenario, predicted final Tre closely agrees with observed values suggesting minimal heat strain (Tre approximately 38.0 degrees C). In contrast, dehydrated (3%) unacclimated individuals show moderate levels of heat strain (Tre approximately 38.5 degrees C) for this same scenario. During the re-entry/landing and emergency egress scenarios, dehydrated unacclimated individuals are predicted to exhibit excessive heat strain (Tre > 39.0 degrees C). Thermal tolerance time is predicted to be only 6 min during emergency egress if individuals are dehydrated and unacclimated to heat while wearing the LES. If heat transfer values for space operations clothing are known, NASA can use this prediction model to help avoid undue heat strain involving astronauts for most scenarios during spaceflight.

Acclimatization↗

PubMed2003

Detection of lung cancer with volatile markers in the breath.

STUDY OBJECTIVES: To evaluate volatile organic compounds (VOCs) in the breath as tumor markers in lung cancer. Alkanes and monomethylated alkanes are oxidative stress products that are excreted in the breath, the catabolism of which may be accelerated by polymorphic cytochrome p450-mixed oxidase enzymes that are induced in patients with lung cancer. DESIGN: Combined case-control and cross-sectional study. SETTING: Five academic pulmonary medicine services in the United States and the United Kingdom. PATIENTS AND PARTICIPANTS: One hundred seventy-eight bronchoscopy patients and 41 healthy volunteers. INTERVENTION: Breath samples were analyzed by gas chromatography and mass spectroscopy to determine alveolar gradients (ie, the abundance in breath minus the abundance in room air) of C4-C20 alkanes and monomethylated alkanes. MEASUREMENTS: Patients with primary lung cancer (PLC) were compared to healthy volunteers, and a predictive model was constructed using forward stepwise discriminant analysis of the alveolar gradients. This model was cross-validated with a leave-one-out jackknife technique and was tested in two additional groups of patients who had not been used to develop the model (ie, bronchoscopy patients in whom cancer was not detected, and patients with metastatic lung cancer [MLC]). RESULTS: Eighty-seven of 178 patients had lung cancer (PLC, 67 patients; MLC, 15 patients; undetermined, 5 patients). A predictive model employing nine VOCs identified PLC with a sensitivity of 89.6% (60 of 67 patients) and a specificity of 82.9% (34 of 41 patients). On cross-validation, the sensitivity was 85.1% (57 of 67 patients) and the specificity was 80.5% (33 of 41 patients). The stratification of patients by tobacco smoking status, histologic type of cancer, and TNM stage of cancer revealed no marked effects. In the two additional tests, the model predicted MLC with a sensitivity of 66.7% (10 of 15 patients), and it classified the cancer-negative bronchoscopy patients with a specificity of 37.4% (34 of 91 patients). CONCLUSIONS: Compared to healthy volunteers, patients with PLC had abnormal breath test findings that were consistent with the accelerated catabolism of alkanes and monomethylated alkanes. A predictive model employing nine of these VOCs exhibited sufficient sensitivity and specificity to be considered as a screen for lung cancer in a high-risk population such as adult smokers.

Alkanes↗

PubMed1992

A computer model to predict the effects of level of nutrition on composition of empty body gain in beef cattle: II. Evaluation of the model.

A computer model developed to predict composition of empty body gain of beef cattle fed at different levels of nutrition was evaluated with data from one unpublished and seven published experiments. These experiments used several breeds of beef cattle growing at rates that varied from negative to fast and various combinations of these growth rates. There was close agreement between observed and simulated absolute treatment means for fatness, except when animals were fed diets low in protein. In this case, experimental animals fed inadequate protein grew at slower rates and became fatter than contemporaries fed adequate protein, whereas the model predicted the opposite. Ability of the model to predict effects of nutrition not associated with changes in BW was evaluated using the proportion of residual variation in experimental fat percentage accounted for by the model, after fitting linear and quadratic terms for empty BW. The model accounted for 13.8 to 56.2% of the residual variation in observed fatness in four experiments in which significant differences in fatness were observed among nutritional treatments after accounting for differences in empty BW. Regression of observed fatness on model-simulated fatness resulted in regression coefficients that were positive and close to 1 in these four experiments. This suggests that the model can accurately predict some of the effects of nutrition on fatness that are not associated with changes in empty BW.

Adipose Tissue↗

PubMedDate not supplied

Development of a multivariate statistical model to predict peroxisome proliferation in the rat, based on urinary 1H-NMR spectral patterns.

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD(+)) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.

Animals↗

PubMed2025

Computational modeling of human genetic variants in mice.

Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.

Journal Article↗

PubMed2006

Prediction of intra-twin birth weight discordance by binary logistic regression analysis.

AIMS: Identification of women at high risk of intra-twin birth weight discordance is helpful in obstetric care of these pregnancies. The aim of this study is to establish an intra-twin birth weight discordance prediction model. METHODS: We created an intra-twin birth weight discordance prediction model by logistic regression, based on the 1995-1997 register twin birth data of the USA. The twin sets were randomly divided into two groups: group 1 to establish the prediction model and group 2 to validate the prediction model. Intra-twin birth weight discordance was defined as birth weight discordance > 25%. The prediction model was validated by receiver operating characteristic curve. RESULTS: A birth weight discordance prediction model including maternal age (beta = 0.069), parity (beta = 0.250), fetal gender concordance (beta = 0.041), maternal hypertension (beta = 0.368), eclampsia (beta = 0.316), other medical complication (beta = 0.165), and smoking (beta = 0.164) was established, yielded a 0.558 area under the receiver operating characteristic curve. The sensitivity, specificity, and positive predictive values were 38.1, 69.7, and 10.8%, respectively, at the cut-off value of 0.09 in group 2. CONCLUSION: A birth weight discordance prediction model that includes seven variables available during pregnancy has been established with acceptable diagnostic performance.

Area Under Curve↗

PubMedDate not supplied

Incorporating predictions of individual patient risk in clinical trials.

A risk prediction model is a statistical technique that gives a predicted probability of a certain event for an individual patient. Prediction models outperform the traditional risk classification systems that work by assigning patients into risk groups based on the presence or absence of particular risk factors, such as stage of disease. As such, risk prediction models have a number of important possible uses in clinical trials. For Phase II studies, prediction models can help adjust comparisons with historical control groups for differences in case mix. For Phase III studies, prediction models can ensure that accrued patients are at sufficiently high risk. This improves statistical power and avoids unethical inclusion of low-risk patients. We also propose that prediction models could potentially be used for applying the results of Phase III trials to individual patients. Clinical decisions could be informed by individualized estimates of treatment benefit, rather than by average treatment effects.

Clinical Trials, Phase III as Topic↗

PubMed2000

Validation of a model for prediction of skin temperatures in footwear.

A model for foot skin temperature prediction was evaluated on the basis of 2 experiments on subjects at various environmental temperatures (light seated manual work at -10.7 degrees C (Study 1), and a short walking period in combination with standing and sitting at +2.8 degrees C, -11.8 degrees C and -24.6 degrees C (Study 2), with boots of 3 insulation levels. Insulation of the footwear was measured on a thermal foot model. Predicted and measured data showed a relatively good correlation (r = 0.87) at the 2 colder conditions in Study 2. The environmental temperature of 2.8 degrees C was not low enough at the chosen activity for a considerable foot skin temperature drop. In Study 1 the predicted temperature stayed higher for the whole exposure period and the difference between the predicted and the measured foot skin temperatures grew proportionally with time, while subsequent warm-up curves at room temperature were almost parallel. In Study 1 the correlation was 0.95. However, the paired t-test showed usually significant differences between measured and predicted foot skin temperatures. The insulation values from thermal foot measurements can be used in the model calculations. Lotens' foot model is lacking activity as direct input parameter, however, the blood flow is used instead (effect through Tcore). The Lotens foot model can give reasonable foot skin temperature values if the model limitations are considered. Due to the lack of activity level input, it will be difficult to make any good estimation of foot skin temperature during intermittent exercise. The rate of the foot temperature recovery after cold exposure was somewhat overestimated in the model--the warm-up of the feet of the subjects started later and was slower in the beginning of the warm-up than in the prediction. It could be useful to develop the model further by taking into consideration various wetness and activity levels.

Foot↗

PubMed2005

A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China.

A model was developed using remote sensing and geographic information system technologies for habitat identification of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum, in the Poyang Lake area, China. In a first step, two multi-temporal Landsat TM 5 satellite images, one from the wet and the second from the dry season, were visually classified into different land-use types. Next, the normalized difference vegetation index was extracted from the images and the tasseled-cap transformation was employed to derive the wetness feature. Our model predicted an estimated 709 km2 of the marshlands in Poyang Lake as potential habitats for O. hupensis. Near-ground temperature measurements in April and August yielded a range of 22.8-24.2 degrees C, and pH values of 6.0-8.5 were derived from existing records. Both climatic features represent suitable breeding conditions for the snails. Preliminary validation of the model at 10 sites around Poyang Lake revealed an excellent accuracy for predicting the presence of O. hupensis. We used the predicted snail habitats as centroids and established buffer zones around them. Villages with an overall prevalence of S. japonicum below 3% were located more than 1200m away from the centroids. Furthermore, a gradient of high-to-low prevalence was observed with increasing distance from the centroids. In conclusion, the model holds promise for identifying high risk areas of schistosomiasis japonica and may become an important tool for the ongoing national schistosomiasis control programme. The model is of particular relevance for schistosome-affected regions that lack accurate surveillance capabilities and are large enough to be detected at most commercially available remote sensing scales.

Animals↗

PubMed2001

Evaluation of the efficacy of extrapolation population modeling to predict the dynamics of Americamysis bahia populations in the laboratory.

An age-classified projection matrix model has been developed to extrapolate the chronic (28-35 d) demographic responses of Americamysis bahia (formerly Mysidopsis bahia) to population-level response. This study was conducted to evaluate the efficacy of this model for predicting the population behavior of A. bahia held (for more than three generations) under controlled laboratory exposure conditions. The research involved the performance of a standard life-cycle test and a multigenerational (greater than three mysid generations, 55 d) assay using A. bahia to experimentally evaluate model predictions regarding population-level risks of chemical exposure. The organic compound para-nonylphenol was chosen as the chemical stressor in assays. This compound is a ubiquitous contaminant and suspected endocrine disruptor. Utilizing data obtained during the standard life-cycle test, aggregate estimates of population growth rate (lambda) and measured p-nonylphenol concentration were used to develop an exposure-response model of population-level effects. These estimates provided the basis of predictions for the long-term dynamics of mysid populations exposed to p-nonylphenol. The veracity of the mysid population model was evaluated through quantitative comparisons of model predictions based on the life-cycle test with dynamics of the experimental populations (multigenerational assay results). The results indicate that the population model was able to project within a few micrograms per liter the concentration where population-level effects would begin to occur (projected 16 micrograms/L from the model vs measured 19 micrograms/L from the multigenerational assay).

Aging↗

PubMed2004

Model for predicting the impact of gadolinium on plasma calcium measured by the o-cresolphthalein method.

BACKGROUND: Gadolinium formulations, which are administered as contrast agents in magnetic resonance imaging examinations, interfere with colorimetric serum calcium determinations. METHODS: We performed an in vitro study to determine the extent to which three gadolinium formulations-gadodiamide (Omniscan), gadopentetate dimeglumine (Magnevist), and gadoversetamide (OptiMARK)-affect measurements by two methods that use o-cresolphthalein (Dade Behring, Inc. and Roche Diagnostics) and one that uses arsenazo dye (Equal Diagnostics). We also compared values from the o-cresolphthalein methods for 116 samples from patients administered gadodiamide. RESULTS: Magnevist did not affect any of the methods evaluated, whereas Omniscan and OptiMARK were identical in their effects. For the Dade method, the differences from the control sample were < or =4.0 and 7.0 mg/L at 0.25 and 0.5 mmol/L gadolinium, respectively. For the Roche method, the differences were 19, 9.0, and 5.0 mg/L at 0.5, 0.25, and 0.125 mmol/L gadolinium, respectively. Falsely increased calcium values were seen when samples were measured by the arsenazo-based method: differences were 6.0 and 3.0 mg/L at 1.0 and 0.5 mmol/L gadolinium. Using patient data collected at our institution, we were able to generate a model for predicting, from a patient's glomerular filtration rate and the time elapsed since administration, the impact of Omniscan on calcium measurements by the o-cresolphthalein method from Roche Diagnostics. CONCLUSIONS: The predictive model can be used to calculate, in patients who have received gadodiamide, the minimum length of time to wait before blood collection to avoid pseudohypocalcemia when the Roche o-cresolphthalein method is used.

Calcium↗

PubMed2002

Experimental and computational screening models for prediction of aqueous drug solubility.

PURPOSE: To devise experimental and computational models to predict aqueous drug solubility. METHODS: A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. RESULTS: The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/ mL, covering a range of almost seven log10 units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R2 of 0.91 and RMSEtr of 0.61) was devised. CONCLUSIONS: A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.

Chemical Phenomena↗