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PubMed1999

CAFASP-1: critical assessment of fully automated structure prediction methods.

The results of the first Critical Assessment of Fully Automated Structure Prediction (CAFASP-1) are presented. The objective was to evaluate the success rates of fully automatic web servers for fold recognition which are available to the community. This study was based on the targets used in the third meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP-3). However, unlike CASP-3, the study was not a blind trial, as it was held after the structures of the targets were known. The aim was to assess the performance of methods without the user intervention that several groups used in their CASP-3 submissions. Although it is clear that "human plus machine" predictions are superior to automated ones, this CAFASP-1 experiment is extremely valuable for users of our methods; it provides an indication of the performance of the methods alone, and not of the "human plus machine" performance assessed in CASP. This information may aid users in choosing which programs they wish to use and in evaluating the reliability of the programs when applied to their specific prediction targets. In addition, evaluation of fully automated methods is particularly important to assess their applicability at genomic scales. For each target, groups submitted the top-ranking folds generated from their servers. In CAFASP-1 we concentrated on fold-recognition web servers only and evaluated only recognition of the correct fold, and not, as in CASP-3, alignment accuracy. Although some performance differences appeared within each of the four target categories used here, overall, no single server has proved markedly superior to the others. The results showed that current fully automated fold recognition servers can often identify remote similarities when pairwise sequence search methods fail. Nevertheless, in only a few cases outside the family-level targets has the score of the top-ranking fold been significant enough to allow for a confident fully automated prediction. Because the goals, rules, and procedures of CAFASP-1 were different from those used at CASP-3, the results reported here are not comparable with those reported in CASP-3. Nevertheless, it is clear that current automated fold recognition methods can not yet compete with "human-expert plus machine" predictions. Finally, CAFASP-1 has been useful in identifying the requirements for a future blind trial of automated served-based protein structure prediction.

Algorithms↗
PubMed1999

Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.

A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%. This is a 1% improvement over PHD, which was the best single method evaluated. Segment Overlap Accuracy (SOV) is 75.4% for the consensus method on the 396-protein set. The secondary structure definition method DSSP defines 8 states, but these are reduced by most authors to 3 for prediction. Application of the different published 8- to 3-state reduction methods shows variation of over 3% on apparent prediction accuracy. This suggests that care should be taken to compare methods by the same reduction method. Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross-validation of secondary structure prediction methods without artifacts due to internal homology. A fully automatic World Wide Web service that predicts protein secondary structure by a combination of methods is available via http://barton.ebi.ac.uk/.

Algorithms↗
PubMed1997

Artificial neural networks improve the accuracy of cancer survival prediction.

BACKGROUND: The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models. METHODS: For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy. RESULTS: For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869). CONCLUSIONS: Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites.

Breast Neoplasms↗
PubMed1997

Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma.

BACKGROUND: Neural networks have been used to predict outcome in cancer patients. Their accuracy compared with standard statistical methods has not been fully assessed. METHODS: In this study, the authors examined the ability to predict the outcome of surgery in 620 patients with nonsmall cell lung carcinoma (NSCLC) by a genetic algorithm neural network (GANN) using Bayes' theorem compared with logistic regression, and the predictive value of tumor volume measures in addition to standard indices such as histologic type and stage. Predictive methods were compared by examining accuracy of classifying target outcome of patients living or dead at 6, 12, 18, and 24 months after surgery. RESULTS: GANN was a significantly better predictor of outcome than logistic regression at all time points (McNemar, P < 0.01). Measures of tumor volume produced significant improvement in the prediction of 12-, 18-, and 24-month time points with GANN, and at 18- and 24-month time points with logistic regression (Wilcoxon matched pairs signed rank test, P < 0.02). CONCLUSIONS: In this study of surgically treated NSCLC patients, outcome predictions were significantly improved by including measures of tumor volume. For predicting individual patient outcome, GANN was found to be highly accurate and significantly better than logistic regression.

Aged↗
PubMed1999

Predicting binding energetics from structure: looking beyond DeltaG degrees.

Structure-based design of pharmaceuticals requires the ability to predict ligand affinity based on knowledge of structure. The primary term of interest is the binding affinity constant, K, or the free energy of binding, DeltaG degrees. It is common to attempt to predict DeltaG degrees based on empirically derived terms which represent common contributions such as the hydrophobic effect, hydrogen bonding, and conformational entropy. Although these approaches have met with some success, when they fail it is difficult to know which parameter(s) need refinement. Confidence in these approaches is also limited by the fact that DeltaG degrees typically is made up of compensating enthalpic and entropic terms, DeltaH degrees and DeltaS degrees, so that accurate prediction of a DeltaG degrees value may be fortuitous and may not indicate a reasonable understanding of the underlying relationship between structure and affinity. This is further complicated by the fact that both DeltaH degrees and DeltaS degrees are strongly temperature dependent through the heat capacity change, DeltaCp. In order to avoid these difficulties, we attempt to use structural data to predict DeltaH degrees, DeltaS degrees, and DeltaCp from which DeltaG degrees can be calculated as a function of temperature. The predictions are then compared to experimentally determined values. These calculations have been applied to several systems by ourselves and others. Systems include the binding of angiotensin II to an antibody, the dimerization of interleukin-8, and the binding of inhibitors to aspartic and serine proteases. Overall the calculations are very successful, and suggest that our understanding of the contributions of the hydrophobic effect, hydrogen bonding, and conformational entropy are quite good. Several of these systems show a strong dependence of the binding energetics on pH, indicative of changes in proton affinity of ionizable groups upon binding. It is critical to account for these protonation contributions to the binding energetics in order to assess the reliability of any computational prediction of energetics from structure. Methods have been developed for determining the energetics of proton binding using isothermal titration calorimetry. The availability of these methods provides a means of understanding how protein structure can modify the pKa's of ionizable groups. This information will further add to our understanding of structural energetic relationships and our ability accurately to predict binding affinities.

Energy Metabolism↗
PubMedDate not supplied

Sonographic EFW and macrosomia: is there an optimum formula to predict diabetic fetal macrosomia?

OBJECTIVE: To compare the accuracy of 31 published formulas for estimated fetal weight (EFW) in predicting macrosomia (birthweight 4,000 gm or more) in infants of diabetic mothers. METHODS: The study population comprised 165 women with gestational or pregestational diabetes who had sonograms to estimate fetal weight after 36 weeks of gestation and within 2 weeks of delivery. Three measures of accuracy were compared: 1) area under the receiver operating characteristic (ROC) curve relating EFW to macrosomia, 2) systematic error, and 3) absolute error. For each measure, the 31 formulas were rank-ordered from 1 (best) to 31 (worst). For each formula, the three rank scores were summed to give a total score. The formula with the lowest total score was considered the "best" formula. RESULTS: Macrosomia occurred in 49 cases (30%). Areas under the ROC curves ranged from 0.8361-0.8978. Differences in areas were not significantly different between the 31 formulas. The 1986 formula of Ott et al. had the lowest total score. Using this "best" formula, an EFW of 4,000 gm or more had a sensitivity of 45% to predict macrosomia and a positive predictive value of 81%. To achieve 90% sensitivity with this formula would have required diagnosis of macrosomia with an EFW of 3,535 gm or more, but this would have comprised 46% of the population with a 42% false-positive rate. All 31 formulas were better at predicting macrosomia than predictions based on gestational age alone, and 28 were better than predictions based on abdominal circumference alone. CONCLUSIONS: All 31 formulas for EFW had comparably poor accuracy for prediction of macrosomia. Delivery decisions based on EFW will often be in error. Future studies should determine whether specific sonographic measurements, ratios, or differences are better than EFW or birthweight as predictors of birth trauma.

Birth Injuries↗
PubMed1983

The use of a tumor colony assay in predicting chemotherapeutic drug response in murine bladder cancer.

A tumor colony assay (TCA) was evaluated for its ability to predict anticancer drug responses in an N-[4-(5-nitro-2-furyl)-2-thiazolyl]-formamide (FANFT)-induced murine bladder tumor (MBT) model. Ten anticancer drugs were evaluated in vivo and in vitro using four MBT cell lines (40 drug responses). Using the optimum criteria for drug response, the TCA accurately predicted drug responses in the murine model 65% of the time with a true-positive predictive rate of 54% and a true-negative predictive rate of 70%. Drug testing tumor cells immediately on removal from the mouse resulted in a true-positive predictive rate of 77% and a true-negative predictive rate of 100%. The authors conclude that the TCA can predict response to chemotherapy in the MBT model and may be useful in screening investigation compounds for the subsequent evaluation in this murine bladder tumor model.

Animals↗
PubMed1986

Do the predictions for cancer incidence come true? Experience from Finland.

The observed occurrence of cancer in Finland in 1980 was compared with that predicted by extrapolation of the trends in cancer incidence by site in 1957 to 1968. The predictions had been constructed as 90% confidence intervals. Of the mutually exclusive 33 incidence predictions, 22 (67%) included the observed incidence rate. The incidence rate was successfully predicted for intestinal, pancreatic, and urinary cancers, as well as for lymphomas, whereas incidence forecasts concerning cancers of the lung, breast, and cervix uteri, and melanomas of the skin did not come true. The incidence of male lung cancer did not increase as was expected, breast cancer incidence increased even more than expected, and the decrease in the incidence of cervical cancer was steeper than expected. Because of failure in the population forecast, only 55% of the predictions (90% confidence intervals) for the numbers of new cancer cases included the observed number. For administrative purposes it would be important for the predictions to come true. On the other hand, the successes or failures of the predictions related to specific cancers may be used to study the effect of changes in etiologic factors, diagnostic methods, or the definition of cancer, as well as the effect of mass screening or other intervention measures upon the cancer incidence. Extrapolation as a method of forecasting cancer incidence is adequate as far as the control of random error is concerned when only short-term forecasts, ranging from 5 to 10 years, are needed.

Aged↗
PubMed2000

Evaluation of "external" predictability of an in vitro-in vivo correlation for an extended-release formulation containing metoprolol tartrate.

The purpose of this study was to examine the external predictability of an in vitro-in vivo correlation (IVIVC) for a metoprolol hydrophilic matrix extended-release formulation, with an acceptable internal predictability, in the presence of a range of formulation/manufacturing changes. In addition, this report evaluated the predictability of the IVIVC for another formulation of metoprolol tartrate differing in its release mechanism. Study 1 examined the scale up of a matrix extended-release tablet from a 3-kg small batch (I) to a 50-kg large batch (II). The second study examined the influence of scale and processing changes [3-kg small batch with fluid bed granulation and drying (III); 80-kg large batch with high shear granulation and microwave drying (IV), and a formulation with an alternate release mechanism formulated as a multiparticulate capsule (V)]. In vitro dissolution of all formulations (I-V) was conducted with a USP apparatus I at pH 6.8 and 150 rpm. Subjects received the metoprolol formulations, and serial blood samples were collected over 48 h and analyzed by a validated HPLC assay using fluorescence detection. A previously developed IVIVC was used to predict plasma profiles. Prediction errors (PE) were <10% for C(max) and area under the curve (AUC) of concentration versus time for I, II, and IV. The C(max) for III was slightly underestimated (11.7%); however, the PE of the AUC was <10%. Formulation V displayed a PE for C(max) > 20% and an AUC within 5% of observed values. The low PEs for C(max) and AUC observed for I-IV strongly suggest that the metoprolol IVIVC is externally valid, predictive of alternate processing methods (IV), scale-up (II, III), and allows the in vitro dissolution data to be used as a surrogate for validation studies. However, the lack of predictability for V supports the contention that IVIVCs are formulation specific.

Adrenergic beta-Antagonists↗
PubMed2003

Predicting nose projection and pronasale position in facial approximation: a test of published methods and proposal of new guidelines.

Many prediction guidelines exist in facial approximation for determining the soft-tissue features of the face, and the reliability of each is generally unknown. This study examines four published and commonly used soft-tissue prediction guidelines for estimating nose projection, two of which also estimate the position of the pronasale. The methods tested are those described by: 1) Gerasimov ([1971] The Face Finder; London: Hutchinson & Co.), using the distal third of the nasal bones and the nasal spine; 2) Krogman ([1962] The Human Skeleton in Forensic Medicine; Springfield: Charles C. Thomas), using the average soft-tissue depth at midphiltrum, plus three times the length of the nasal spine (and a variation of this technique: plus three times the distance of the tip of the nasal spine from the nasal aperture); 3) Prokopec and Ubelaker ([2002] Forensic Sci Commun 4:1-4), using the reflected profile line of the nasal aperture; and 4) George ([1987] J Forensic Sci 32:1305-1330), using a variation of the Goode method. Four identical hard-tissue tracings were made of 59 adult lateral head cephlograms (29 males, mean age 24, SD 10 years; 30 females, mean age 23, SD 5 years) on separate sheets of tracing paper. One soft-tissue tracing was also made for each radiograph. All tracings were marked with three identical reference points. Soft-tissue tracings were isolated from one of us (C.N.S.), who attempted under blind conditions to predict pronasale position and nose projection on the hard-tissue tracings, using the soft-tissue prediction guides above. Actual soft-tissue tracings were then compared to each of the predicted tracings, and differences in projection/pronasale position were measured. Results indicate that for nose projection, methods 3 and 4 performed well, while methods 1 and 2 performed poorly. Features which are most related to nose projection/pronasale are described in this paper, as are regression equations generated from these variables that predict pronasale/nose projection better than the traditional methods mentioned above. The results of this study are significant because they: 1) indicate that the popular facial approximation methods used to build the nose are inaccurate and produce incorrect nose anatomy; and 2) indicate that the new pronasale prediction methods developed here appear to have less error than traditional methods.

Adult↗
PubMed2006

The effect of discordance among violence and general recidivism risk estimates on predictive accuracy.

INTRODUCTION: Previous research has shown that the prediction of short-term inpatient violence is negatively affected when clinicians' inter-rater agreement is low and when confidence in the estimate of risk is low. This study examined the effect of discordance between risk assessment instruments used to predict long-term general and violence risk in offenders. METHODS: The Psychopathy Checklist - Revised (PCL-R), Level of Service Inventory - Revised (LSI-R), Violence Risk Appraisal Guide (VRAG), and the General Statistical Information on Recidivism (GSIR) were the four risk-prediction instruments used to predict post-release general and violent recidivism within a sample of 209 offenders. RESULTS: The findings lend empirical support to the assumption that predictive accuracy is threatened where there is discordance between risk estimates. Discordance between instruments had the impact of reducing predictive accuracy for all instruments except the GSIR. Further, the influence of discordance was shown to be greater on certain instruments over others. Discordance had a moderating effect on both the PCL-R and LSI-R but not on the VRAG and GSIR. CONCLUSIONS: There is a distinct advantage when attempting to predict recidivism to employing measures such as the LSI-R, which includes dynamic variables and intervention-related criminogenic domains, over a measure purely of fixed characteristics, such as the GSIR; however, if there is discordance between the risk estimates, caution should be exercised and more reliance on the more static historically based instrument may be indicated.

Adolescent↗
PubMed2006

Development and validation of nomograms for predicting residual tumor size and the probability of successful conservative surgery with neoadjuvant chemotherapy for breast cancer.

BACKGROUND: Neoadjuvant chemotherapy (NACT) increases the likelihood that breast conservation therapy for breast cancer patients will be successful. There is no available nomogram to predict breast conservation after NACT. The aim of the current study was to develop and validate nomograms for predicting residual tumor size and probability of a patient becoming eligible for breast conservation surgery after NACT. METHODS: A total of 1147 patients treated at M. D. Anderson Cancer Center (Houston, TX) and the Institut Gustave Roussy (Villejuif, France) who received anthracycline with or without paclitaxel NACT were included in the analysis. Clinicopathologic data from 1 series were used to construct logistic regression models for breast conservation and residual tumor size < 3 cm after NACT and were validated on an independent series. RESULTS: The discrimination and the calibration of the nomogram for predicting the probability of residual tumor size < 3 cm after anthracycline-based NACT were good when applied to the validation set (concordance index = 0.79; U-index = 10(-3)). The discrimination of the nomogram for predicting eligibility for breast conservation therapy was also good (concordance index = 0.67). However, the calibration had to be adjusted to take into account global rates of breast conservation surgery. A second nomogram adapted to preoperative chemotherapy regimens containing paclitaxel was established. The concordance index of the nomogram for predicting breast conservation was 0.71 (P < 10(-6)) for the independent dataset and the calibration was also good. The confrontation of both nomograms showed that predictions were highly correlated (r = 0.97), suggesting that eligibility for breast conservation therapy was independent of the preoperative chemotherapy regimen used. CONCLUSIONS: Nomograms were developed for breast cancer patients who received NACT to predict residual tumor size and whether the patient would thus become eligible for breast conservation therapy. These tools may be useful when counseling patients about treatment options, and a web-based interface is now available to help guide patients and physicians in these decisions.

Anthracyclines↗
PubMed1997

Comparing the predicted and observed properties of proteins encoded in the genome of Escherichia coli K-12.

Mining the emerging abundance of microbial genome sequences for hypotheses is an exciting prospect of "functional genomics". At the forefront of this effort, we compared the predictions of the complete Escherichia coli genomic sequence with the observed gene products by assessing 381 proteins for their mature N-termini, in vivo abundances, isoelectric points, molecular masses, and cellular locations. Two-dimensional gel electrophoresis (2-DE) and Edman sequencing were combined to sequence Coomassie-stained 2-DE spots representing the abundant proteins of wild-type E. coli K-12 strains. Greater than 90% of the abundant proteins in the E. coli proteome lie in a small isoelectric point and molecular mass window of 4-7 and 10-100 kDa, respectively. We identified several highly abundant proteins, YjbJ, YjbP, YggX, HdeA, and AhpC, which would not have been predicted from the genomic sequence alone. Of the 223 uniquely identified loci, 60% of the encoded proteins are proteolytically processed. As previously reported, the initiator methionine was efficiently cleaved when the penultimate amino acid was serine or alanine. In contrast, when the penultimate amino acid was threonine, glycine, or proline, cleavage was variable, and valine did not signal cleavage. Although signal peptide cleavage sites tended to follow predicted rules, the length of the putative signal sequence was occassionally greater than the consensus. For proteins predicted to be in the cytoplasm or inner membrane, the N-terminal amino acids were highly constrained compared to proteins localized to the periplasm or outer membrane. Although cytoplasmic proteins follow the N-end rule for protein stability, proteins in the periplasm or outer membrane do not follow this rule; several have N-terminal amino acids predicted to destabilize the proteins. Surprisingly, 18% of the identified 2-DE spots represent isoforms in which protein products of the same gene have different observed pI and M(r), suggesting they are post-translationally processed. Although most of the predicted and observed values for isoelectric point and molecular mass show reasonable concordance, for several proteins the observed values significantly deviate from the expected values. Such discrepancies may represent either highly processed proteins or misinterpretations of the genomic sequence. Our data suggest that AhpC, CspC, and HdeA exist as covalent homomultimers, and that IcdA exists as at least three isoforms even under conditions in which covalent modification is not predicted. We enriched for proteins based on subcellular location and found several proteins in unexpected subcellular locations.

Amino Acid Sequence↗
PubMed2004

Time to include time to death? The future of health care expenditure predictions.

Government projections of future health care expenditures--a great concern given the aging baby-boom generation--are based on econometric regressions that control explicitly for age but do not control for end-of-life expenditures. Because expenditures increase dramatically on average at the end of life, predictions of future cost distributions based on regressions that omit time to death as an explanatory variable will be biased upward (or, more explicitly, the coefficients on age will be biased upward) if technology or other social factors continue to prolong life. Although health care expenditure predictions for a current sample will not be biased, predictions for future cohorts with greater longevity will be biased upwards, and the magnitude of the bias will increase as the expected longevity increases. We explore the empirical implications of incorporating time to death in longitudinal models of health expenditures for the purpose of predicting future expenditures. Predictions from a simple model that excludes time to death and uses current life tables are 9% higher than from an expanded model controlling for time to death. The bias increases to 15% when using projected life tables for 2020. The predicted differences between the models are sufficient to justify reassessment of the value of inclusion of time to death in models for predicting health care expenditures.

Aged↗
PubMed2007

Lung cancer risk prediction: a tool for early detection.

Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected. For some cancers, substantial progress in individual risk estimation has already been made. However, relatively few models have been developed to predict lung cancer risk beyond effects of age and smoking. This paper reviews published models for lung cancer risk prediction, discusses their potential contribution to clinical and research settings and suggests improvements to the risk modeling strategy for lung cancer. The sensitivity and specificity of existing cancer risk models is less than optimal. Improvement in individual risk prediction is important for selection of individuals for prevention or early detection interventions. In addition to smoking, factors related to occupational exposure, personal medical history and family history of cancer can add to the predictive power. A good risk prediction model is one that can identify a small fraction of the population in which a large proportion of the disease cases will occur. In the future, genetic and other biological markers are likely to be useful, although they will require rigorous evaluation. Validation is essential to establish the predictive effect and for ongoing monitoring of the model's continued relevance.

Early Diagnosis↗
PubMed1994

Prediction of the neonatal crown-heel length from femur diaphysis length measurements.

The usefulness of the Rossavik growth model in predicting crown-heel length (CHL) was evaluated in 50 women with normal singleton pregnancies in a Dutch population. The femur diaphysis lengths (FDL) were predicted assuming growth cessation at 38, 39, 39.5, and 40 weeks, menstrual age (MA), and at birth using Rossavik growth models determined from two second-trimester FDL measurements. Predicted CHLs were then calculated from predicted FDLs using six different equations. Predicted CHLs were compared with the actual neonatal CHLs and the percent differences calculated. The growth potential realization index (GPRI) values were also determined. With all six equations, regression analysis revealed a significant relationship between the percent differences and birth ages for those infants delivering after 38 and 39 weeks, MA, respectively. The signs of the slopes of the regression lines were negative, indicating continued growth of these fetuses. With the Vintzileos equation, no evidence of continued growth was obtained after 39.5 weeks, MA. The systematic prediction error at this time point was 0.9%, whereas the random error was 3.3%. The mean GPRICHL was 99%, with a 95% range of 93% to 104%. These findings indicate that the CHL can be predicted with a high degree of accuracy in this Dutch population if the appropriate growth cessation age and FDL-CHL function are used.

Adolescent↗
PubMed1994

Evaluation of muscle force prediction models of the lumbar trunk using surface electromyography.

Optimization-based models for prediction of muscle forces in the lumbar region of the torso are used to estimate the forces acting on spinal motion segments, especially for asymmetric tasks. The objectives of this study were to determine (a) which of four torso model formulations best predicted the electromyographic data, (b) the difference in muscular contribution to spinal compression force for the four models, and (c) the effect of using the lowest possible muscle stress bound in the model formulation. An approach for the investigation of competing optimization model formulations was developed and was illustrated with electromyographic data from static asymmetric loading conditions. This method is based on (a) the choice of experimental conditions in which models predict decidedly different muscle forces, and (b) the ability to ensure that the experimental conditions are such that the minimum number of assumptions about the force-electromyogram relationship must be made in order to choose between competing model predictions. Of the four models analyzed, only the formulation with an objective function that was the sum of cubed muscle stresses predicted the electromyographic data acceptably. The muscular contribution to spinal compression force predicted by these models differed by as much as 160% for some experimental conditions. The use of the lowest possible muscle stress bound does not appear to predict muscle forces that are in agreement with electromyographic data.

Adult↗
PubMed2001

Prediction of the disposition of midazolam in surgical patients by a physiologically based pharmacokinetic model.

The aim of this study was to predict the disposition of midazolam in individual surgical patients by physiologically based pharmacokinetic (PBPK) modeling and explore the causes of interindividual variability. Tissue-plasma partition coefficients (k(p)) were scaled from rat to human values by a physiologically realistic four-compartment model for each tissue, incorporating the measured unbound fraction (f(u)) of midazolam in the plasma of each patient. Body composition (lean body mass versus adipose tissue) was then estimated in each patient, and the volume of distribution at steady state (V(dss)) of midazolam was calculated. Total clearance (CL) was calculated from unbound intrinsic CL, f(u), and estimated hepatic blood flow. Curves of midazolam plasma concentration versus time were finally predicted by means of a perfusion-limited PBPK model and compared with measured data. In a first study on 14 young patients undergoing surgery with modest blood loss, V(dss) was predicted with an only 3.4% mean error (range -24-+39%) and a correlation between predicted and measured values of 0.818 (p < 0.001). Scaling of k(p) values by the four-compartment model gave better predictions of V(dss) than scaling using unbound k(p). In the PBPK modeling, the mean +/- standard deviation (SD) prediction error for all data was 9.7 +/- 33%. In a second study with 10 elderly patients undergoing orthopedic surgery, hemodilution and blood loss led to a higher f(u) of midazolam. The PBPK modeling correctly predicted a marked increase in V(dss), a smaller increase in CL, and a prolonged terminal half-life of midazolam, as compared with findings in the first study. Interindividual variation in the disposition of midazolam could thus in part be related to the physiological characteristics of the patients and the f(u) of the drug in their plasma.

Adjuvants, Anesthesia↗