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At least 19 recordsLinked to original sources

PubMed1999

From fold predictions to function predictions: automation of functional site conservation analysis for functional genome predictions.

A database of functional sites for proteins with known structures, SITE, is constructed and used in conjunction with a simple pattern matching program SiteMatch to evaluate possible function conservation in a recently constructed database of fold predictions for Escherichia coli proteins (Rychlewski L et al., 1999, Protein Sci 8:614-624). In this and other prediction databases, fold predictions are based on algorithms that can recognize weak sequence similarities and putatively assign new proteins into already characterized protein families. It is not clear whether such sequence similarities arise from distant homologies or general similarity of physicochemical features along the sequence. Leaving aside the important question of nature of relations within fold superfamilies, it is possible to assess possible function conservation by looking at the pattern of conservation of crucial functional residues. SITE consists of a multilevel function description based on structure annotations and structure analyses. In particular, active site residues, ligand binding residues, and patterns of hydrophobic residues on the protein surface are used to describe different functional features. SiteMatch, a simple pattern matching program, is designed to check the conservation of residues involved in protein activity in alignments generated by any alignment method. Here, this procedure is used to study conservation of functional features in alignments between protein sequences from the E. coli genome and their optimal structural templates. The optimal templates were identified and alignments taken from the database of genomic structural predictions was described in a previous publication (Rychlewski L et al., 1999, Protein Sci 8:614-624). An automated assessment of function conservation is used to analyze the relation between fold and function similarity for a large number of fold predictions. For instance, it is shown that identifying low significance predictions with a high level of functional residue conservations can be used to extend the prediction sensitivity for fold prediction methods. Over 100 new fold/function predictions in this class were obtained in the E. coli genome. At the same time, about 30% of our previous fold predictions are not confirmed as function predictions, further highlighting the problem of function divergence in fold superfamilies.

Algorithms↗

PubMed2004

Predicting functional outcome in acute stroke: comparison of a simple six variable model with other predictive systems and informal clinical prediction.

BACKGROUND: Statistical models that predict functional outcome after stroke using six simple variables (SSV) have recently been developed and validated. OBJECTIVE: To compare the accuracy of these models with other simple ways of predicting outcome soon after stroke. METHODS: The SSV model for being alive and independent (modified Rankin score < or =2) six months or one year after stroke was compared with predictions based on a model that included only age and Oxford community stroke project classification, with predictions based on conscious level and urinary continence, and with informal clinical predictions made by clinicians interested in stroke. Predictions were compared in an independent hospital based cohort of stroke patients using receiver operator characteristic (ROC) curves. RESULTS: The SSV model at six months had a significantly greater area under the curve (0.84) than the model with only age and stroke classification (0.75). Predictions based on conscious level and urinary continence were no better than those of the SSV model and were unable to predict subjects with a high probability of good outcome. The sensitivity and specificity for informal clinical predictions at one year lay on or below the SSV model curve, implying that the SSV model was at least as good as clinical predictions. CONCLUSIONS: The SSV models performed as well as or better than other simple predictive systems. These models will be useful in epidemiological studies but should not be used to guide clinical management until their impact on patient care and outcome has been evaluated.

Activities of Daily Living↗

PubMed2005

Simple cystatin C-based prediction equations for glomerular filtration rate compared with the modification of diet in renal disease prediction equation for adults and the Schwartz and the Counahan-Barratt prediction equations for children.

BACKGROUND: Serum creatinine is the most commonly used marker for estimation of glomerular filtration rate (GFR). To compensate for its drawbacks as a GFR marker, several prediction equations including several parameters are being used, with the Modification of Diet in Renal Disease (MDRD), Schwartz, and Counahan-Barratt equations being the ones most widely accepted for estimation of relative GFR in mL x min(-1) x (1.73 m(2))(-1). The present study analyzes whether these GFR prediction equations for adults and children might be replaced by simple prediction equations based on plasma concentrations of cystatin C. METHODS: Data from 536 patients (0.3-93 years), consecutively referred for determination of GFR by an invasive gold standard procedure, were used for the analysis. Calculations of bias (median percentage of error), correlation (adjusted R(2)), and percentage of estimates within 30% and 50% of measured GFR were used in the comparisons. RESULTS: A cystatin C-based prediction equation using only concentration in mg/L and a prepubertal factor: GFR [mL x min(-1) x (1.73 m(2))(-1)] = 84.69 x cystatin C (mg/L)(-1.680) x 1.384 (if a child <14 years) assessed GFR equally well or better than the simplified MDRD, the Schwartz, and the Counahan-Barratt prediction equations for the adult (> or =18 years) and juvenile groups of the investigated cohort. Age did not influence the cystatin C-based prediction equation for adults, whereas gender did, but with a factor close to unity (0.948 for females). CONCLUSION: A GFR prediction equation based solely on cystatin C (in mg/L) and a prepubertal factor might replace the simplified MDRD prediction equation for adults and the Schwartz and Counahan-Barratt prediction equations for children.

Adolescent↗

PubMed2000

Topology prediction of Brucella abortus Omp2b and Omp2a porins after critical assessment of transmembrane beta strands prediction by several secondary structure prediction methods.

In order to propose a reliable model for Brucella porin topology, several structure prediction methods were evaluated in their ability to predict porin topology. Four porins of known structure were selected as test-cases and their secondary structure delineated. The specificity and sensitivity of 11 methods were separately evaluated. Our critical assessment shows that some secondary structure prediction methods (PHD, Dsc, Sopma) originally designed to predict globular protein structure are useful on porin topology prediction. The overall best prediction is obtained by combining these three "generalist" methods with a transmembrane beta strand prediction technique. This "consensus" method was applied to Brucella porins Omp2b and Omp2a, sharing no sequence homology with any other porin. The predicted topology is a 16-stranded antiparallel beta barrel with Omp2a showing a higher number of negatively charged residue in the exposed loops than Omp2b. Experiments are in progress to validate the proposed topology and the functional hypotheses. The ability of the proposed consensus method to predict topology of complex outer membrane protein is briefly discussed.

Amino Acid Sequence↗

PubMed2004

Sensitivity, specificity, positive predictive value, and negative predictive value of the dipyridamole sestamibi stress test in predicting graft occlusion or > or = 50% new native coronary artery disease in men versus women and in patients aged > or = 65 years versus < 65 years who had prior coronary artery bypass grafting.

The sensitivity, specificity, positive predictive value, and negative predictive value of the dipyridamole sestamibi stress test (DSST) in predicting graft obstruction or > or = 50% new native coronary artery disease (CAD) in 88 men compared with 56 women with prior coronary artery bypass surgery were 96% and 94% (p = NS), 30% and 67% (p = NS), 91% and 96 % (p = NS), 50% and 57% (p = NS), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the DSST in predicting graft obstruction or > or = 50% new native CAD in 92 patients aged < 65 years were 95% and 95% (p = NS), 50% and 40% (p = NS), 96% and 87% (p = NS), and 43% and 67% (p = NS), respectively.

Adult↗

PubMed2001

A simple prediction rule and a neural network model to predict pancreatic beta-cell reserve in young adults with diabetes mellitus.

In the present study we developed and assessed the performance of a simple prediction rule and a neural network model to predict beta-cell reserve in young adults with diabetes. Eighty three young adults with diabetes were included in the study. All were less than 40 years old and without apparent secondary causes of diabetes. The subjects were randomly allocated to 2 groups; group 1 (n = 59) for developing a prediction rule and training a neural network, group 2 (n = 24) for validation purpose. The prediction rule was developed by using stepwise logistic regression. Using stepwise logistic regression and modification of the derived equation, the patient would be insulin deficient if 3(waist circumference in cm) + 4(age at diagnosis) < 340 in the absence of previous diabetic ketoacidosis (DKA) or < 400 in the presence of previous DKA. When tested in the validation set, the prediction rule had positive and negative predictive values of 86.7 per cent and 77.8 per cent respectively with 83.3 per cent accuracy while the ANN model had a positive predictive value of 88.2 per cent and a negative predictive value of 100 per cent with 91.7 per cent accuracy. When testing the performance of the prediction rule and the ANN model compared to the assessment of 23 internists in a subgroup of 9 diabetics whose age at onset was less than 30 years and without a history of DKA, the ANN had the highest ability to predict beta-cell reserve (accuracy = 88.9), followed by the prediction rule (accuracy = 77.8%) and assessments by internists (accuracy = 60.9%). We concluded that beta-cell reserve in young adults with diabetes mellitus could be predicted by a simple prediction rule or a neural network model. The prediction rule and the neural network model can be helpful clinically in patients with mixed clinical features of type 1 and type 2 diabetes.

Adolescent↗

PubMed2002

Evaluating transmembrane topology prediction methods for the effect of signal peptide in topology prediction.

Reported performance of existing transmembrane (TM) topology prediction methods were often based on evaluations which neglected the risk of signal peptides (SP) being predicted as putative TM as well. Here, we evaluated 12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and MPEx) for the effect of SP in prediction performance considering three SP treatments, namely: "remain" (untreated), "removed first", and "removed later". The results showed that the presence of SP significantly affected the prediction performance of the 12 selected TM topology prediction methods for all three predicted attributes (the number of transmembrane segments (TMSs), the number of TMSs plus position, and the N-tail location) and for the predicted topology (combined predictions of three attributes) by causing a reduction in prediction accuracy. In particular, lower prediction accuracies were obtained if SP is left untreated (remain) while significant increases were observed if SP is removed either first or later. However, between "removed first" and "removed later" SP treatments, the difference was statistically insignificant. In addition, we found that machine learning-based prediction methods were less affected by the presence of SP than hydropathy-based methods, but still the potential risk of degrading the prediction performance is there however to a lesser degree. Thus, when performing genome-wide analysis, the SP issue should be addressed during TM topology prediction.

Algorithms↗

PubMedDate not supplied

Prediction of surface and interior regions in proteins--Part II: Predicting secondary structure in regions bound by surface exposed regions.

A new secondary structure prediction algorithm (SecondaryPlot) is described and has been developed for the IBM or compatible computer. This algorithm successfully predicts the boundaries of secondary structural regions by combining hydrophilicity, accessibility and flexibility parameters. Within the limits of these predicted structural regions, five algorithms (Chou and Fasman; Garnier, Osguthorpe and Robson; Delange and Roux; Barkovsky and Bandarin; and Lim) were used to predict helix and sheet regions. All of the algorithms correctly predicted 50-60% of the x-ray defined regions. Predictions were improved by combining the two most successful algorithms of Lim and Chou and Fasman. By combining the boundary predictions (hydrophilicity, accessibility, flexibility and linear polar tripeptide sequences) with secondary structure predictions (Lim, Chou and Fasman), approximately 80% of the x-ray defined structural regions were predicted correctly for 30 proteins (86% for the alpha-class, 79% for the beta-class and 82% for the alpha/beta class of proteins). In our hands, the best of the five algorithms studied to predict the state (helix or sheet) of these structural regions predicted 62% of helical regions and 64% of extended regions in a test set of fifteen proteins. However, SecondaryPlot predicted 78% of helical regions and 71% of extended regions for the same test. SecondaryPlot provides a significant improvement to structure prediction and is a necessary first step in the tertiary structure prediction of proteins.

Algorithms↗

PubMed2006

Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies.

PURPOSE: Available seizure-prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep-wake cycle. METHODS: In 21 patients, each with 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics. RESULTS: A dependency of false predictions on the time of day is shown. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. Combining two reference intervals, one during sleep and one in an awake state, the dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity. No obvious dependence of false predictions was noted on visible epileptic activity, such as spikes, sharp waves, or subclinical ictal patterns. CONCLUSIONS: Changes in the EEG dynamics related to the sleep-wake cycle contribute to limits of specificity of both seizure-prediction methods investigated. This may provide a clue for improving prediction methods in general. The combination of reference states yields promising results and may offer opportunities to increase further the performance of prediction methods.

Adolescent↗

PubMed1985

Performance of Bayesian feedback to forecast lidocaine serum concentration: evaluation of the prediction error and the prediction interval.

The prediction performance of the Bayesian feedback method was evaluated with respect to accuracy and precision, and efficacy and safety (width of the prediction interval) on the basis of 90 predictions in 30 patients treated with lidocaine. The mean of the prediction error (PE) and the root mean squared error (RMSE) served as a measure of accuracy and precision. The variance of the standardized prediction error (SPE) was used to evaluate the estimate of the standard deviation of the prediction error. SPE was defined as PE divided by the standard deviation of the predicted concentration. The standard error of RMSE and of the variance of SPE was determined by bootstrap. The results indicate that the lidocaine serum concentration at 12 hr (C2) after starting continuous infusion can be predicted with high accuracy and precision with a single feedback measurement obtained 2-4 hr (C1) after commencement of treatment: RMSE = 20.6%. Prediction at 24 hr (C3) was less accurate: RMSE = 31.4%. Using both C1 and C2 to predict C3 improved precision (RMSE = 23.4%). The evaluation of the prediction interval revealed that the current algorithm produces an upward biased estimate, probably due to a positive bias in the estimate of the covariance matrix of the parameter estimates. It is suggested that evaluation of prediction performance should include the estimate of the prediction interval.

Bayes Theorem↗

PubMed1999

Prediction of clearance, volume of distribution and half-life by allometric scaling and by use of plasma concentrations predicted from pharmacokinetic constants: a comparative study.

Pharmacokinetic parameters (clearance, CL, volume of distribution in the central compartment, VdC, and elimination half-life, t1/2beta) predicted by an empirical allometric approach have been compared with parameters predicted from plasma concentrations calculated by use of the pharmacokinetic constants A, B, alpha and beta, where A and B are the intercepts on the Y axis of the plot of plasma concentration against time and alpha and beta are the rate constants, both pairs of constants being for the distribution and elimination phases, respectively. The pharmacokinetic parameters of cefpiramide, actisomide, troglitazone, procaterol, moxalactam and ciprofloxacin were scaled from animal data obtained from the literature. Three methods were used to generate plots for the prediction of clearance in man: dependence of clearance on body weight (simple allometric equation); dependence of the product of clearance and maximum life-span potential (MLP) on body weight; and dependence of the product of clearance and brain weight on body weight. Plasma concentrations of the drugs were predicted in man by use of A, B, alpha and beta obtained from animal data. The predicted plasma concentrations were then used to calculate CL, VdC and t1/2beta. The pharmacokinetic parameters predicted by use of both approaches were compared with measured values. The results indicate that simple allometry did not predict clearance satisfactorily for actisomide, troglitazone, procaterol and ciprofloxacin. Use of MLP or the product of clearance and brain weight improved the prediction of clearance for these four drugs. Except for troglitazone, VdC and t1/2beta predicted for man by use of the allometric approach were comparable with measured values for the drugs studied. CL, VdC and t1/2beta predicted by use of pharmacokinetic constants were comparable with values predicted by simple allometry. Thus, if simple allometry failed to predict clearance of a drug, so did the pharmacokinetic constant approach (except for actisomide). The results of this study indicate that caution should be employed in interpreting plasma concentrations predicted for a drug in man by use of pharmacokinetic constants obtained in animals.

Animals↗

PubMed2001

Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model.

BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit nurses at the time of admission to the unit between February and September 1995 for 235 consecutive adult nontrauma patients. Scores on the Mortality Prediction Model were calculated on admission. Data on 85 of the 235 patients were analyzed by using descriptive, chi 2, and correlational statistics. Nurses' predictions of function were compared with patients' actual outcomes 6 months after admission. RESULTS: Nurses' predictions of survival were comparable to those of emergency physicians and superior to those obtained by using the objective tool. Years of nursing experience had no relationship to attitudes toward aggressiveness of care. Nurses accurately predicted functional outcomes in 52% of the followed-up cases. Intensive care physicians were more accurate than nurses and emergency physicians in predicting survival. All predictions made by clinicians were superior to those obtained by using the model. CONCLUSIONS: Nurses can predict survival outcomes as accurately as physicians do. Greater sensitivity and specificity are necessary before clinical judgment or predictive tools can be considered as screens for determining aggressiveness of care.

Adolescent↗

PubMed1995

Predicting unbound phenytoin concentrations in patients receiving valproic acid: a comparison of two prediction methods.

OBJECTIVE: To compare the predictive performance of 2 equations that estimate unbound (free) phenytoin plasma concentrations when valproic acid (VPA) and phenytoin are administered concurrently. DESIGN: Eighty-eight adults receiving VPA and phenytoin concurrently were included in the study. Steady-state plasma concentration measurements of total phenytoin, total VPA, and unbound phenytoin were collected prospectively in the inpatient group (group 1) and retrospectively in the outpatient group (group 2). Using the equations developed by Haidukewych and May, unbound phenytoin concentrations were calculated. The mean predicted unbound phenytoin concentrations then were compared with mean actual unbound phenytoin concentrations measured in the laboratory. Identical assays were performed to measure unbound phenytoin, total phenytoin, and VPA from each patient group. SETTING/PARTICIPANTS: Antiepileptic drug concentration measurements were collected from 43 inpatients (mean age 34.8 y) from the epilepsy unit at Abbott Northwestern Hospital and 45 outpatients (mean age 37.3 y) at the MINCEP Epilepsy Care clinic, Minneapolis, MN. MAIN OUTCOME MEASURES: Mean prediction error (MPE) and mean squared error (MSE) were calculated to determine which equation was the least biased and most precise in predicting unbound phenytoin when total VPA and phenytoin concentrations are known. Linear regression of predicted unbound phenytoin on measured unbound phenytoin values determined the correlation coefficients (r). A paired Student's t-test also was performed comparing mean predicted unbound phenytoin concentration with mean actual unbound phenytoin concentrations in both groups. RESULTS: The MPE from May's equation was -0.49 and -0.45 for groups 1 and 2, respectively; using the Haidukewych equation, MPE was -0.02 and 0.08 for groups 1 and 2, respectively. The MSE using May's equation was 0.34 for both groups. Using the Haidukewych equation, group 1 MSE was 0.07, and for group 2, 0.12. Correlation coefficients were more than 0.91 (p < 0.001) from each equation in both patient groups. In group 1, mean actual unbound phenytoin concentration was 2.02 micrograms/mL; May's equation predicted 1.52 micrograms/mL (p < 0.001) and the Haidukewych equation predicted 2.00 micrograms/mL (p = 0.64). In group 2, mean actual unbound phenytoin concentration was 2.10 micrograms/mL; May's equation predicted 1.65 micrograms/mL (p < 0.001) and the Haidukewych equation predicted 2.18 micrograms/mL (p = 0.11). CONCLUSIONS: Haidukewych's equation predicts unbound phenytoin concentrations with the least bias and most precision with statistical significance. May's equation consistently underpredicted unbound phenytoin concentrations. Because unbound phenytoin fraction is not constant (and usually more than the expected 10%) in patients comedicated with VPA, unbound phenytoin concentrations cannot be predicted even though total VPA and phenytoin concentrations are known. If unbound phenytoin concentrations cannot be readily measured, Haidukewych's equation is a reliable predictor of unbound phenytoin concentrations.

Adult↗

PubMed1997

Patient-specific predictions of outcomes in myocardial infarction for real-time emergency use: a thrombolytic predictive instrument.

BACKGROUND: Thrombolytic therapy can be life-saving in patients with acute myocardial infarction. However, if given too late or insufficiently selectively, it may provide little benefit but still cause serious complications and incur substantial costs. OBJECTIVE: To develop a thrombolytic predictive instrument for real-time use in emergency medical service settings that could 1) identify patients likely to benefit from thrombolysis and 2) facilitate the earliest possible use of this therapy. DESIGN: Creation and validation of logistic regression-based predictive instruments based on secondary analysis of clinical data. PATIENTS: 4911 patients who had acute myocardial infarction and ST-segment elevation on electrocardiogram; 3483 received thrombolytic therapy. MEASUREMENTS: Data were obtained from 13 major clinical trials and registries and directly from medical records, including electrocardiograms obtained at presentation. Input variables include presenting clinical and electrocardiography features; predictive models generate probabilities for acute (30-day) mortality if and if not treated with thrombolysis, 1-year mortality rates if and if not treated with thrombolysis, cardiac arrest if and if not treated with thrombolysis, thrombolysis-related intracranial hemorrhage, and thrombolysis-related major bleeding episode requiring transfusion. Together, these models constitute the thrombolytic predictive instrument. RESULTS: The predictive models generated the following mean predictions for patients in the Thrombolytic Predictive instrument Database: 30-day mortality rate, 7.1%; 1-year mortality rate, 10.9%; rate of cardiac arrest, 3.7%; rate of thrombolysis-related intracranial hemorrhage. 0.6%; and rate of other thrombolysis-related major bleeding episodes, 5.0%. They discriminated with between persons having and those not having the predicted outcome; areas under the receiver-operating characteristic (ROC) curve were between 0.77 and 0.84 for the five outcomes. Calibration between each instrument's predicted and observed served rates was excellent. Validation of the predictive instruments of 30-day and 1-year mortality, done on a separate test dataset, yielded areas under the ROC curve of 0.76 for each CONCLUSIONS: After the basic features of a clinical presentation are entered into a computerized electrocardiograph, the predictions of the thrombolytic predictive instrument can be printed on the electrocardiogram report. This decision aid may facilitate earlier and more appropriate use of thrombolytic therapy in patients with acute myocardial infarction.

Cerebral Hemorrhage↗

PubMed2006

Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics.

In learning goal-directed behaviors, an agent has to consider not only the reward given at each state but also the consequences of dynamic state transitions associated with action selection. To understand brain mechanisms for action learning under predictable and unpredictable environmental dynamics, we measured brain activities by functional magnetic resonance imaging (fMRI) during a Markov decision task with predictable and unpredictable state transitions. Whereas the striatum and orbitofrontal cortex (OFC) were significantly activated both under predictable and unpredictable state transition rules, the dorsolateral prefrontal cortex (DLPFC) was more strongly activated under predictable than under unpredictable state transition rules. We then modelled subjects' choice behaviours using a reinforcement learning model and a Bayesian estimation framework and found that the subjects took larger temporal discount factors under predictable state transition rules. Model-based analysis of fMRI data revealed different engagement of striatum in reward prediction under different state transition dynamics. The ventral striatum was involved in reward prediction under both unpredictable and predictable state transition rules, although the dorsal striatum was dominantly involved in reward prediction under predictable rules. These results suggest different learning systems in the cortico-striatum loops depending on the dynamics of the environment: the OFC-ventral striatum loop is involved in action learning based on the present state, while the DLPFC-dorsal striatum loop is involved in action learning based on predictable future states.

Brain↗

PubMed2002

Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution.

In drug discovery and nonclinical development the volume of distribution at steady state (V(ss)) of each novel drug candidate is commonly determined under in vivo conditions. Therefore, it is of interest to predict V(ss) without conducting in vivo studies. The traditional description of V(ss) corresponds to the sum of the products of each tissue:plasma partition coefficient (P(t:p)) and the respective tissue volume in addition to the plasma volume. Because data on volumes of tissues and plasma are available in the literature for mammals, the other input parameters needed to estimate V(ss) are the P(t:p)'s, which can potentially be predicted with established tissue composition-based equations. In vitro data on drug lipophilicity and plasma protein binding are the input parameters used in these equations. Such a mechanism-based approach would be particularly useful to provide first-cut estimates of V(ss) prior to any in vivo studies and to explore potential unexpected deviations between sets of predicted and in vivo V(ss) data, when the in vivo data become available during the drug development process. The objective of the present study was to use tissue composition-based equations to predict rat and human V(ss) prior to in vivo studies for 123 structurally unrelated compounds (acids, bases, and neutrals). The predicted data were compared with in vivo data obtained from the literature or at Roche. Overall, the average ratio of predicted-to-experimental rat and human V(ss) values was 1.06 (SD = 0.817, r = 0.78, n = 147). In fact, 80% of all predicted values were within a factor of two of the corresponding experimental values. The drugs can therefore be separated into two groups. The first group contains 98 drugs for which the predicted V(ss) were within a factor of two of those experimentally determined (average ratio of 1.01, SD = 0.39, r = 0.93, n = 118), and the second group includes 25 other drugs for which the predicted and experimental V(ss) differ by a factor larger than two (average ratio of 1.32, SD = 1.74, r = 0.42, n = 29). Thus, additional relevant distribution processes were neglected in predicting V(ss) of drugs of the second group. This was true especially in the case of some cationic-amphiphilic bases. The present study is the first attempt to develop and validate a mechanistic distribution model for predicting rat and human V(ss) of drugs prior to in vivo studies.

Animals↗

PubMed1991

Weak correlation between predictive power of individual sequence patterns and overall prediction accuracy in proteins.

Patterns in amino acid properties (polar, hydrophobic, etc.) that characterize secondary structure motifs are derived from a database containing 75 protein structures, with the aim of circumventing the limitations due to data base size so as to increase structure prediction score. Many such sequence-structure associations with high intrinsic predictive power are found, which turn out to be correct 78% of the time when applied individually to proteins outside the learning set. Based on these associations, a prediction method is developed, which reaches the score of 62% on the 3 states alpha-helix, beta-strand, and loop, without using additional constraints. Though this score is quite good compared to that of other available prediction methods, it is much lower than could be expected from the high intrinsic predictive power of the associations used. The reasons underlying this surprising result, which indicate that prediction score and intrinsic predictive power are only weakly coupled, are discussed. It is also shown that the size of the present database still seriously limits prediction scores, even when property patterns are used, and that higher scores are expected in large databases. Clues are provided on the relative influence of neglecting spatial interactions on prediction efficiency, suggesting that, in sufficiently large databases, predicted secondary structures would correspond to those formed early in the folding process. This hypothesis is tested by confronting present predictions with available experimental data on early protein folding intermediates and on small peptides that adopt a relatively stable conformation in water. Although admittedly there are still too few such data, results suggest that the hypothesis might be well founded.

Amino Acid Sequence↗

PubMed2003

Predicting the emergence of drug-resistant HSV-2: new predictions.

BACKGROUND: Mathematical models can be used to predict the emergence and transmission of antiviral resistance. Previously it has been predicted that high usage of antivirals (in immunocompetent populations) to treat Herpes Simplex Virus type 2 (HSV-2) would only lead to fairly low levels of antiviral resistance. The HSV-2 predictions were based upon the assumption that drug-resistant strains of HSV-2 would be less infectious than drug-sensitive strains but that the drug-resistant strains would not be impaired in their ability to reactivate. Recent data suggest that some drug-resistant strains of HSV-2 are likely to be impaired in their ability to reactivate. OBJECTIVES: (1) To predict the effect of a high usage of antivirals on the prevalence of drug-resistant HSV-2 under the assumption that drug-resistant strains will be less infectious than drug-sensitive strains of HSV-2 and also have an impaired ability to reactivate. (2) To compare predictions with previous published predictions. METHODS: We generated theoretical drug-resistant HSV-2 strains that were attenuated (in comparison with drug-sensitive strains) in both infectivity and ability to reactivate. We then used a transmission model to predict the emergence and transmission of drug-resistant HSV-2 in the immunocompetent population assuming a high usage of antivirals. RESULTS: Our predictions are an order of magnitude lower than previous predictions; we predict that even after 25 years of high antiviral usage only 5 out of 10,000 immunocompetent individuals will be shedding drug-resistant virus. Furthermore, after 25 years, 52 cases of HSV-2 would have been prevented for each prevalent case of drug-resistant HSV-2. CONCLUSIONS: The predicted levels of drug-resistant HSV-2 for the immunocompetent population are so low that it seems unlikely that cases of drug-resistant HSV-2 will be detected.

Antiviral Agents↗