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Journal articles on the topic 'Dose prediction'

Author: Grafiati

Published: 24 June 2021

Last updated: 17 February 2022

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1

Marek, Elizabeth, Jeremiah D. Momper, Ronald N. Hines, Cheryl M. Takao, Joan C. Gill, Vera Pravica, Andrea Gaedigk, Gilbert J. Burckart, and Kathleen A. Neville. "Prediction of Warfarin Dose in Pediatric Patients: An Evaluation of the Predictive Performance of Several Models." Journal of Pediatric Pharmacology and Therapeutics 21, no. 3 (May 1, 2016): 224–32. http://dx.doi.org/10.5863/1551-6776-21.3.224.

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OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R2), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose. RESULTS: Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from −2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R2 = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses. CONCLUSIONS: Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed.

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2

Swartz, Conrad M. "Drug Dose Prediction With Flexible Test Doses." Journal of Clinical Pharmacology 31, no. 7 (July 1991): 662–67. http://dx.doi.org/10.1002/j.1552-4604.1991.tb03753.x.

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3

&NA;. "IV aminoglycoside dose prediction." Inpharma Weekly &NA;, no. 995 (July 1995): 18. http://dx.doi.org/10.2165/00128413-199509950-00043.

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Qasim, Husam, Sophie Sominsky, Aharon Lubetsky, Noa Markovits, Chun Li, C. Stein, Hillel Halkin, Eva Gak, Ronen Loebstein, and Daniel Kurnik. "Effect of the VKORC1 D36Y variant on warfarin dose requirement and pharmacogenetic dose prediction." Thrombosis and Haemostasis 108, no. 10 (2012): 781–88. http://dx.doi.org/10.1160/th12-03-0151.

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SummaryPharmacogenetic dosing algorithms help predict warfarin maintenance doses, but their predictive performance differs in different populations, possibly due to unsuspected population-specific genetic variants. The objectives of this study were to quantify the effect of the VKORC1 D36Y variant (a marker of warfarin resistance previously described in 4% of Ashkenazi Jews) on warfarin maintenance doses and to examine how this variant affects the performance of the International Warfarin Pharmacogenetic Consortium (IWPC) dose prediction model. In 210 Israeli patients on chronic warfarin therapy recruited at a tertiary care centre, we applied the IWPC model and then added D36Y genotype as covariate to the model (IWPC+D36Y) and compared predicted with actual doses. Median weekly warfarin dose was 35 mg (interquartile range [IQR], 24.5 to 52.5 mg). Among 16 heterozygous D36Y carriers (minor allele frequency = 3.8%), warfarin weekly dose was increased by a median of 43.7 mg (IQR, 40.5 to 47.2 mg) compared to non-carriers after adjustment for all IWPC parameters, a greater than two-fold dose increase. The IWPC model performed suboptimally (coefficient of determination R2=27.0%; mean absolute error (MAE), 14.4 ± 16.2 mg/ week). Accounting for D36Y genotype using the IWPC+D36Y model resulted in a significantly better model performance (R2=47.2%, MAE=12.6±12.4 mg/week). In conclusion, even at low frequencies, variants with a strong impact on warfarin dose may greatly decrease the performance of a commonly used dose prediction model. Unexpected discrepancies of the performance of universal prediction models in subpopulations should prompt searching for unsuspected confounders, including rare genetic variants.

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Laidlaw, J., P. Bentham, G. Khan, V. Staples, A. Dhariwal, B. Coope, E. Day, C. Fear, C. Marley, and J. Stemman. "A comparison of stimulus dosing methods for electroconvulsive therapy." Psychiatric Bulletin 24, no. 5 (May 2000): 184–87. http://dx.doi.org/10.1192/pb.24.5.184.

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Aims and MethodsA prospective study comparing initial electroconvulsive therapy treatment doses determined by empirical dose titration with estimates derived from two simple dose prediction methods and a fixed-dose regimen (275 mC).ResultsThirty-three patients had seizure thresholds between 25 mC and 403 mC. The dose titration method led to a mean initial treatment dose of 195 mC that was intermediate between those predicted by the age method (275 mC) and the half-age method (137 mC). Estimates were within acceptable limits in 33% of cases for the age method, 64% for the half-age method and 40% for the fixed-dose method.Clinical ImplicationsEither dose prediction or dose titration methods may be more appropriate in different clinical situations. The half-age method appears to be a more accurate predictor of optimum initial treatment dose.

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Gizynska, M., D. Blatkiewicz, B. Czyzew, M. Galecki, M. Gil-Ulkowska, P. Kukolowicz, and M. Ziemek. "EP-1510: Cumulated dose prediction." Radiotherapy and Oncology 115 (April 2015): S822—S823. http://dx.doi.org/10.1016/s0167-8140(15)41502-6.

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7

Matsumoto, Hiroshi, Yoshikuni Yakabe, Fumiyo Saito, Koichi Saito, Kayo Sumida, Masaru Sekijima, Koji Nakayama, Hideki Miyaura, Masanori Otsuka, and Tomoyuki Shirai. "New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats." Cancer Informatics 10 (January 2011): CIN.S7789. http://dx.doi.org/10.4137/cin.s7789.

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We have previously shown the hepatic gene expression profiles of carcinogens in 28-day toxicity tests were clustered into three major groups (Group-1 to 3). Here, we developed a new prediction method for Group-1 carcinogens which consist mainly of genotoxic rat hepatocarcinogens. The prediction formula was generated by a support vector machine using 5 selected genes as the predictive genes and predictive score was introduced to judge carcinogenicity. It correctly predicted the carcinogenicity of all 17 Group-1 chemicals and 22 of 24 non-carcinogens regardless of genotoxicity. In the dose-response study, the prediction score was altered from negative to positive as the dose increased, indicating that the characteristic gene expression profile emerged over a range of carcinogen-specific doses. We conclude that the prediction formula can quantitatively predict the carcinogenicity of Group-1 carcinogens. The same method may be applied to other groups of carcinogens to build a total system for prediction of carcinogenicity.

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8

Xie, Cheng, Ling Xue, Yuzhen Zhang, Jianguo Zhu, Ling Zhou, Yongfu Hang, Xiaoliang Ding, Bin Jiang, and Liyan Miao. "Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients." Pharmacogenomics 21, no. 1 (January 2020): 23–32. http://dx.doi.org/10.2217/pgs-2019-0124.

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Aim: To compare the prediction performance of different warfarin dosing algorithms based on Chinese patients. Materials & methods: A total of 18 algorithms were tested in 325 patients. The predictive efficacy of selected algorithms was evaluated by calculating the percentage of patients whose predicted dose fell within ±20% of their actual stable warfarin dose and the mean absolute error. Results: The percentage within ± 20% and the mean absolute error of the algorithms ranged from 11.9 to 41.2% and -0.20 (-0.29 to -0.11) mg/d to -1.63 (-1.75 to -1.50) mg/d. The algorithms established by Miao et al. and Wei et al. had optimal predictive performance. Conclusion: Algorithms based on geographical populations might be more suitable for the prediction of stable warfarin doses in local patients.

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9

Holford, Nick H. G., Shu C. Ma, and Brian J. Anderson. "Prediction of morphine dose in humans." Pediatric Anesthesia 22, no. 3 (December 28, 2011): 209–22. http://dx.doi.org/10.1111/j.1460-9592.2011.03782.x.

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10

OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "RADIATION DOSE PREDICTION FOR DETACHED HOUSES." Journal of Environmental Engineering (Transactions of AIJ) 82, no. 735 (2017): 481–89. http://dx.doi.org/10.3130/aije.82.481.

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11

MIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, no. 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.

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12

Lippi, Giuseppe, Gian Luca Salvagno, and Gian Cesare Guidi. "Genetic Factors for Warfarin Dose Prediction." Clinical Chemistry 53, no. 9 (September 1, 2007): 1721–22. http://dx.doi.org/10.1373/clinchem.2007.092338.

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13

Mitchel, R. E. J. "Radiation Risk Prediction and Genetics: The Influence of the TP53 Gene in vivo." Dose-Response 3, no. 4 (October 1, 2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.007.

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Risk prediction and dose limits for human radiation exposure are based on the assumption that risk is proportional to total dose. However, there is concern about the appropriateness of those limits for people who may be genetically cancer prone. The TP53 gene product functions in regulatory pathways for DNA repair, cell cycle checkpoints and apoptosis, processes critical in determining ionizing radiation risk for both carcinogenesis and teratogenesis. Mice that are deficient in TP53 function are cancer prone. This review examines the influence of variations in TP53 gene activity on cancer and teratogenic risk in mice exposed to radiation in vivo, and compares those observations to the assumptions and predictions of radiation risk inherent in the existing system of radiation protection. Current assumptions concerning a linear response with dose, dose additivity, lack of thresholds and dose rate reduction factors all appear incorrect at low doses. TP53 functional variations can further modify radiation risk from either high or low doses, or risk from radiation exposures combined with other stresses, and those modifications can result in both quantitative and qualitative changes in risk.

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14

Mortazavi, S. M. J., Fatemeh Aminiazad, Hossein Parsaei, and Mohammad Amin Mosleh-Shirazi. "AN ARTIFICIAL NEURAL NETWORK-BASED MODEL FOR PREDICTING ANNUAL DOSE IN HEALTHCARE WORKERS OCCUPATIONALLY EXPOSED TO DIFFERENT LEVELS OF IONIZING RADIATION." Radiation Protection Dosimetry 189, no. 1 (February 26, 2020): 98–105. http://dx.doi.org/10.1093/rpd/ncaa018.

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Abstract We presented an artificial intelligence-based model to predict annual effective dose (AED) value of health workers. Potential factors affecting AED and the results of annual blood tests were collected from 91 radiation workers. Filter-based feature selection strategy revealed that the eight factors plate, red cell distribution width (RDW), educational degree, nonacademic course in radiation protection (hour), working hours per month, department and the number of procedures done per year and work in radiology department or not (0,1) were the most important predictors for AED. The prediction model was developed using a multilayer perceptron neural network and these prediction parameters as inputs. The model provided favorable accuracy in predicting AED value while a regression model did not. There was a strong linear relationship between the predicted AED values and the measured doses (R-value =0.89 for training samples and 0.86 for testing samples). These results are promising and show that artificial neural networks can be used to improve/facilitate dose estimation process.

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15

Schwartz, Michael, Katharina Sixel, Charlene Young, Andras Kemeny, David Forster, Lee Walton, and Edmee Franssen. "Prediction of Obliteration of Arteriovenous Malformations after Radiosurgery: the Obliteration Prediction Index." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 24, no. 2 (May 1997): 106–9. http://dx.doi.org/10.1017/s0317167100021417.

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ABSTRACT:Objective:To describe the response to single dose photon stereotactic radiosurgery of arteriovenous malformations (AVMs) so that the probability of success or failure of treatment may be predicted for the individual patient.Method:The obliteration prediction index (OPI) was calculated for AVMs by dividing the marginal dose of radiation in Gray (Gy) by the lesion diameter in centimetres in cohorts of 42 patients treated with the modified linear accelerator at Toronto-Sunnybrook Regional Cancer Centre and 394 patients treated with the gamma unit at the Royal Hallamshire Hospital, Sheffield, United Kingdom. Patients were grouped into ranges by OPI and the proportion of success and failure was calculated for each group. An exponential function [P = 1-A•e(-B-OPI)] was fitted to the data by the least squares method.Results:Despite systematic differences in radiation treatment, that is, marginal doses of 15 and 20 Gy in Toronto and most Sheffield patients with a marginal dose of 25 Gy, the resultant data points exhibited similar behaviour.Conclusion:The function [P = 1-A•e(-B-OPI)] partly describes the biological effect of radiation and is independent of the radiation device used. Radiosurgery centres can use this model to facilitate predictions of successful treatment for individual patients.

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OSCAR, THOMAS P. "Development and Validation of a Predictive Microbiology Model for Survival and Growth of Salmonella on Chicken Stored at 4 to 12°C†." Journal of Food Protection 74, no. 2 (February 1, 2011): 279–84. http://dx.doi.org/10.4315/0362-028x.jfp-10-314.

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Salmonella spp. are a leading cause of foodborne illness. Mathematical models that predict Salmonella survival and growth on food from a low initial dose, in response to storage and handling conditions, are valuable tools for helping assess and manage this public health risk. The objective of this study was to develop and to validate the first predictive microbiology model for survival and growth of a low initial dose of Salmonella on chicken during refrigerated storage. Chicken skin was inoculated with a low initial dose (0.9 log) of a multiple antibiotic-resistant strain of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 4 to 12°C for 0 to 10 days. A general regression neural network (GRNN) model that predicted log change of Salmonella Typhimurium DT104 as a function of time and temperature was developed. Percentage of residuals in an acceptable prediction zone, from −1 (fail-safe) to 0.5 (fail-dangerous) log, was used to validate the GRNN model by using a criterion of 70% acceptable predictions. Survival but not growth of Salmonella Typhimurium DT104 was observed at 4 to 8°C. Maximum growth of Salmonella Typhimurium DT104 during 10 days of storage was 0.7 log at 9°C, 1.1 log at 10°C, 1.8 log at 11°C, and 2.9 log at 12°C. Performance of the GRNN model for predicting dependent data (n = 163) was 85% acceptable predictions, for predicting independent data for interpolation (n = 77) was 84% acceptable predictions, and for predicting independent data for extrapolation (n = 70) to Salmonella Kentucky was 87% acceptable predictions. Thus, the GRNN model provided valid predictions for survival and growth of Salmonella on chicken during refrigerated storage, and therefore the model can be used with confidence to help assess and manage this public health risk.

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Schurr, James W., Anne-Marie Muske, Craig A. Stevens, Sarah E. Culbreth, Katelyn W. Sylvester, and Jean M. Connors. "Derivation and Validation of Age- and Body Mass Index-Adjusted Weight-Based Unfractionated Heparin Dosing." Clinical and Applied Thrombosis/Hemostasis 25 (January 1, 2019): 107602961983348. http://dx.doi.org/10.1177/1076029619833480.

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Unfractionated heparin dosing is unpredictable and subject to numerous pharmacokinetic changes including distribution and metabolic changes associated with obesity and age. Weight-based dosing is commonly used to better predict the dose for a patient when targeting a therapeutic range. A dosing equation that adjusts weight-based doses for age and body mass index may improve therapeutic dose prediction. We conducted a 2-phase observational study with a derivation and validation period to develop an equation to adjust weight-based unfractionated heparin for age and body mass index to target a therapeutic activated partial thromboplastin time of 60 to 80 seconds. The first phase retrospectively identified patients who acheived therapeutic anticoagulation and utilized linear regression to determine a predictive equation for weight-based dosing that adjusts for age and body mass index. The second phase prospectively identified patients in an observational manner and compared the dose of unfractionated heparin on which they became therapeutic against both the weight-based dose and the predicted dose adjusted for age and body mass index. The correlation between predictive age and body mass index adjusted dose and actual therapeutic dose was 0.703 compared to the correlation between the empiric weight-based dose and actual therapeutic dose which was 0.532 ( P = .05). Age and body mass index adjusted weight-based dosing significantly improved therapeutic dose prediction for unfractionated heparin. Further study in a prospective, randomized trial is warranted for validation of this approach in a real world setting.

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Madakasira, Sudhakar, and Prabhaker G. Khazanie. "Reliability of amitriptyline dose prediction based on single-dose plasma levels." Clinical Pharmacology and Therapeutics 37, no. 2 (February 1985): 145–49. http://dx.doi.org/10.1038/clpt.1985.26.

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19

Skarpman Munter, Johanna, and Jens Sjölund. "Dose-volume histogram prediction using density estimation." Physics in Medicine and Biology 60, no. 17 (August 25, 2015): 6923–36. http://dx.doi.org/10.1088/0031-9155/60/17/6923.

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20

OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "MONTE CARLO CALCULATION FOR RADIATION DOSE PREDICTION." Journal of Environmental Engineering (Transactions of AIJ) 81, no. 727 (2016): 835–43. http://dx.doi.org/10.3130/aije.81.835.

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21

Hines, J. W., L. W. Townsend, and T. F. Nichols. "SPE dose prediction using locally weighted regression." Radiation Protection Dosimetry 116, no. 1-4 (December 20, 2005): 131–34. http://dx.doi.org/10.1093/rpd/nci010.

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22

Hines, J. W., L. W. Townsend, and T. F. Nichols. "SPE dose prediction using locally weighted regression." Radiation Protection Dosimetry 116, no. 1-4 (December 20, 2005): 232–35. http://dx.doi.org/10.1093/rpd/nci278.

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23

Nemec, Mary Alice, Susan C. Sorrells, Thomas J. Prihoda, and Robert L. Talbert. "An Objective Method of Compliance Assessment with Metered-Dose Inhalers." DICP 23, no. 2 (February 1989): 128–32. http://dx.doi.org/10.1177/106002808902300204.

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Evaluation of compliance with metered-dose inhalers (MDIs) is especially difficult. We investigated the correlation between serial weight change of the MDI canister and the number of doses delivered. Canister weight strongly correlated with number of activations for three different bronchodilators (metaproterenol, r2 = 0.9965; albuterol, r2 = 0.9984; terbutaline, r2 = 0.9913). To validate these results, a one-month trial designed to mimic patient use was conducted. Nine aerosol bronchodilator MDIs (three each of terbutaline, albuterol, and metaproterenol) were evaluated by three volunteers, each given one of each type of MDI. Number of activations were recorded in a diary and canisters were weighed weekly. At the end of the four-week period the number of activations were determined from weekly canister weight. Predicted activation numbers, calculated from both regression line and baseline weight, were compared to actual activation number. Statistical analysis was done using analysis of variance. Predicted number of activations for all three drugs ranged from 77 to 125 percent of observed and did not significantly differ depending on the type of bronchodilator (p = 0.3340). The method of prediction, regression intercept or baseline weight, led to significantly different predictions (p = 0.0569). The interaction between the method of prediction and type of bronchodilator was significant (p = 0.0197). Using either method, the actual number of doses dispensed can be predicted within 25 percent.

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24

Yan, Aixia, Zhi Wang, Jiaxuan Li, and Meng Meng. "Human Oral Bioavailability Prediction of Four Kinds of Drugs." International Journal of Computational Models and Algorithms in Medicine 3, no. 4 (October 2012): 29–42. http://dx.doi.org/10.4018/ijcmam.2012100104.

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In the development of drugs intended for oral use, good drug absorption and appropriate drug delivery are very important. Now the predictions for drug absorption and oral bioavailability follow similar approach: calculate molecular descriptors for molecules and build the prediction models. This approach works well for the prediction of compounds which cross a cell membrane from a region of high concentration to one of low concentration, but it does not work very well for the prediction of oral bioavailability, which represents the percentage of an oral dose which is able to produce a pharmacological activity. The models for bioavailability had limited predictability because there are a variety of pharmacokinetic factors influencing human oral bioavailability. Recent study has shown that good quantitative relationship could be obtained for subsets of drugs, such as those that have similar structure or the same pharmacological activity, or those that exhibit similar absorption and metabolism mechanisms. In this work, using MLR (Multiple Linear Regression) and SVM (Support Vector Machine), quantitative bioavailability prediction models were built for four kinds of drugs, which are Angiotensin Converting Enzyme Inhibitors or Angiotensin II Receptor Antagonists, Calcium Channel Blockers, Sodium and Potassium Channels Blockers and Quinolone Antimicrobial Agents. Explorations into subsets of compounds were performed and reliable prediction models were built for these four kinds of drugs. This work represents an exploration in predicting human oral bioavailability and could be used in other dataset of compounds with the same pharmacological activity.

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BERG, R., S. KLASH, and M. GOSSMAN. "Surface dose prediction and verification for IMRT plans using line dose profiles." International Journal of Radiation OncologyBiologyPhysics 60 (September 2004): S590. http://dx.doi.org/10.1016/s0360-3016(04)01891-7.

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26

Changshi, Liu. "Prediction of the bias currents induced by60Co via dose and dose rate." Radiation Effects and Defects in Solids 167, no. 4 (April 2012): 275–80. http://dx.doi.org/10.1080/10420150.2011.642870.

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27

Berg, R., S. Klash, and M. Gossman. "Surface dose prediction and verification for IMRT plans using line dose profiles." International Journal of Radiation Oncology*Biology*Physics 60, no. 1 (September 2004): S590. http://dx.doi.org/10.1016/j.ijrobp.2004.07.587.

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28

van der Velde, J., W. B. Geven, C. Festen, A. L. M. Verbeek, and A. F. J. de Haan. "A Multistate Kalman Filter for Neonatal Clotting Time Prediction and Early Detection of Coagulation Disturbances." Methods of Information in Medicine 38, no. 02 (1999): 113–18. http://dx.doi.org/10.1055/s-0038-1634177.

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AbstractThe multistate Kalman filter was applied to develop a heparin dose proposal system and to detect coagulation disturbances during neonatal extracorporeal membrane oxygenation (ECMO). A system containing this filter was based on the activated clotting time (ACT) values and the heparin doses administered every hour during ECMO. If the ACT value can be predicted accurately from the previous heparin dose, a heparin dose proposal can be given to achieve or maintain the required ACT level. The analysis was done on 6,356 ACT level measurements in 44 ECMO neonates. The multistate Kalman filter technique showed an unbiased prediction of ACT, with a standard deviation of 23 seconds. Two out of three cases of disseminated intravascular coagulation (DIC) were detected. ACT values were predicted sufficiently accurately by the multistate Kalman filter technique to justify a prospective study on the performance of the heparin dose proposal system and its ability to detect DIC.

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Neal, John S., and Lawrence W. Townsend. "Prediction of solar particle event proton doses using early dose rate measurements." Acta Astronautica 56, no. 9-12 (May 2005): 961–68. http://dx.doi.org/10.1016/j.actaastro.2005.01.023.

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Siccardi, Marco, Laura Dickinson, and Andrew Owen. "Validation of Computational Approaches for Antiretroviral Dose Optimization." Antimicrobial Agents and Chemotherapy 60, no. 6 (April 4, 2016): 3838–39. http://dx.doi.org/10.1128/aac.00094-16.

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Strategies for reducing antiretroviral doses and drug costs can support global access, and numerous options are being investigated. Efavirenz pharmacokinetic simulation data generated with a bottom-up physiologically based model were successfully compared with data obtained from the ENCORE (Exercise and Nutritional Interventions for Cardiovascular Health) I clinical trial (efavirenz at 400 mg once per day versus 600 mg once per day). These findings represent a pivotal paradigm for the prediction of pharmacokinetics resulting from dose reductions. Validated computational models constitute a valuable resource for optimizing therapeutic options and predicting complex clinical scenarios.

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Israel, Ora, Zohar Keidar, Rafael Rubinov, Galina Iosilevski, Alex Frenkel, Abraham Kuten, Lise Betman, Gerald M. Kolodny, David Yarnitsky, and Dov Front. "Quantitative Bone Single-Photon Emission Computed Tomography for Prediction of Pain Relief in Metastatic Bone Disease Treated With Rhenium-186 Etidronate." Journal of Clinical Oncology 18, no. 14 (July 14, 2000): 2747–54. http://dx.doi.org/10.1200/jco.2000.18.14.2747.

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PURPOSE: To calculate radiation doses of rhenium-186 (186Re) etidronate in painful bone metastases using quantitative bone single-photon emission computed tomography (SPECT) and to determine the threshold dose for predicting pain relief. We also wanted to determine whether technetium-99m (99mTc) methylene diphosphonate (MDP) concentrations predict radiation doses of 186Re etidronate in painful lesions. MATERIALS AND METHODS: Forty-eight patients with breast and prostate cancer were evaluated. Patients received therapeutic doses of 186Re etidronate. The area under the pain over time curve (AUPC) was measured for 8 weeks after treatment. Response was calculated as the percentage of change in AUPC. Quantitative bone SPECT (QBS)–measured concentration of 186Re etidronate was used for calculating radiation doses. Receiver operating characteristics curve analysis determined the radiation dose threshold that best separated responders from nonresponders. SPECT-measured concentration of 186Re etidronate in the urinary bladder was correlated with its concentration in the voided urine. Concentration of 99mTc MDP was compared with radiation doses to painful metastases. RESULTS: The radiation dose threshold was 2.10 Gy. For a decrease of 50% in the AUPC, the positive predictive value (PPV) of this value was 75% and the negative predictive value (NPV) was 88%. For a decrease in pain of 33%, the PPV was 84% and the NPV was 81%. In prostate cancer patients only, the PPV was 81% and the NPV was 92%. The correlation between in vivo/in vitro measured urine concentration was 0.90. The correlation between 99mTc MDP concentration and radiation doses of 186Re etidronate was 0.92. CONCLUSION: QBS-measured radiation doses of 186Re etidronate in painful metastases are a good predictor of pain relief. Bone SPECT using 99mTc MDP predicts radiation doses delivered by 186Re etidronate.

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George, Alex, Bogdan Dinu, Norma Estrada, Charles Minard, Richard L. Hurwitz, Donald Mahoney, Amber M. Yates, et al. "Ndepth: A Randomized Controlled Trial of a Novel Dose-Prediction Equation to Determine Maximum Tolerated Dose for Hydroxyurea Therapy in Pediatric Patients with Sickle Cell Anemia." Blood 134, Supplement_1 (November 13, 2019): 2267. http://dx.doi.org/10.1182/blood-2019-127414.

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Patients on hydroxyurea who achieve maximum tolerated dose (MTD), defined by a target level of mild myelosuppression, may have greater laboratory and clinical benefits than those maintained on a lower dose. MTD is currently determined by gradual dose escalation, a process that often takes six to twelve months. Using data from a previous cohort of pediatric patients escalated to MTD on hydroxyurea, we have developed an equation incorporating baseline serum creatinine, body mass index, and absolute reticulocyte count to predict individualized MTD for patients initiating therapy. The NDEPTH (Novel Dose Escalation to Predict Treatment with Hydroxyurea) study is a prospective, open-label, randomized controlled trial consisting of two treatment arms: a standard arm utilizing a current published dose-escalation protocol for achieving hydroxyurea MTD; and an alternative treatment arm utilizing the dose-prediction equation to determine MTD prior to initiation of treatment. The primary endpoint of the study is time to MTD for each arm. Additional endpoints include analysis of safety and clinical and laboratory response to hydroxyurea therapy We recruited 70 pediatric patients to the study, 68 of whom were randomized equally to the standard and dose-prediction arms of the study. There were no significant differences in baseline characteristics between study participants in the two arms. Twenty-six study participants in the standard arm and 27 in the dose-prediction arm successfully reached MTD. Mean MTD was significantly higher in the dose-prediction arm than the standard arm (27.1 mg/kg vs. 22.6 mg/kg; P <0.0001). The dose-prediction equation calculated the actual MTD for all study participants reaching MTD with a high degree of accuracy (r = 0.43; p = 0.001). On an intention-to-treat log-rank analysis of time to MTD, median time to MTD or censoring on the standard arm was 25.1 weeks while that on the dose-prediction arm was 16.6 weeks (p = 0.1), revealing a trend towards quicker achievement of MTD in the dose-prediction arm. There were 20 episodes of cytopenias on the standard arm and 11 on the dose-prediction arm during the study period, none of which were clinically significant. The number of clinical adverse events over 12 months were also similar between the two study arms (15 in the standard arm and 9 in the dose-prediction arm). In summary, the dose-prediction equation determined actual MTD with a high degree of accuracy and resulted in a significantly higher final hydroxyurea dose for study participants in the dose-prediction arm than that achieved in the standard arm, indicating that young children may be able to tolerate higher hydroxyurea doses than can be achieved by standard dose escalation. The incidence of adverse clinical and laboratory events was similar between the two study arms. Based on these results, we conclude that our dose-prediction method of determining hydroxyurea MTD can be used to safely and rapidly achieve MTD, obviating the delayed MTD and the requirement for frequent clinical and laboratory monitoring associated with standard dose escalation. Table Disclosures George: Global Blood Therapeutics; Pfizer: Consultancy, Honoraria. Fasipe:Novartis: Consultancy, Honoraria; Pfizer and American College of Emergency Physicians: Research Funding. Ware:Bristol Myers Squibb: Other: Research Drug Donation; Addmedica: Other: Research Drug Donation; Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Novartis: Other: DSMB; CSL Behring: Membership on an entity's Board of Directors or advisory committees; Nova Laboratories: Membership on an entity's Board of Directors or advisory committees.

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Dickinson, Laura, Marta Boffito, David Back, Laura Else, Nils von Hentig, Geraint Davies, Saye Khoo, Anton Pozniak, Graeme Moyle, and Leon Aarons. "Sequential Population Pharmacokinetic Modeling of Lopinavir and Ritonavir in Healthy Volunteers and Assessment of Different Dosing Strategies." Antimicrobial Agents and Chemotherapy 55, no. 6 (March 21, 2011): 2775–82. http://dx.doi.org/10.1128/aac.00887-10.

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ABSTRACTNonlinear mixed-effects modeling was applied to explore the relationship between lopinavir and ritonavir concentrations over 72 h following drug cessation and also to assess other lopinavir and ritonavir dosing strategies compared to the standard 400-mg–100-mg twice-daily dose. Data from 16 healthy volunteers were included. Possible covariates influencing lopinavir and ritonavir pharmacokinetics were also assessed. Data were modeled first separately and then together by using individually predicted ritonavir pharmacokinetic parameters in the final lopinavir model. The model was evaluated by means of a visual predictive check and external validation. A maximum-effect model in which ritonavir inhibited the elimination of lopinavir best described the relationship between ritonavir concentrations and lopinavir clearance (CL/F). A ritonavir concentration of 0.06 mg/liter was associated with a 50% maximum inhibition of the lopinavir CL/F. The population prediction of the lopinavir CL/Fin the absence of ritonavir was 21.6 liters/h (relative standard error, 14.0%), and the apparent volume of distribution and absorption rate constant were 55.3 liters (relative standard error, 10.2%) and 0.57 h−1(relative standard error, 0.39%), respectively. Overall, 92% and 94% of the observed concentrations were encompassed by the 95% prediction intervals for lopinavir and ritonavir, respectively, which is indicative of an adequate model. Predictions of concentrations from an external data set (HIV infected) (n= 12) satisfied predictive performance criteria. Simulated lopinavir exposures at lopinavir-ritonavir doses of 200 mg-150 mg and 200 mg-50 mg twice daily were 38% and 65% lower, respectively, than that of the standard dose. The model allows a better understanding of the interaction between lopinavir and ritonavir and may allow a better prediction of lopinavir concentrations and assessments of different dosing strategies.

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Kajikawa, Tomohiro, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori, Hikaru Nemoto, Suguru Dobashi, Ken Takeda, and Keiichi Jingu. "A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients." Journal of Radiation Research 60, no. 5 (July 19, 2019): 685–93. http://dx.doi.org/10.1093/jrr/rrz051.

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Abstract The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

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Chaudhri, Kanika, Sophie L. Stocker, Kenneth M. Williams, Robert C. McLeay, Deborah J. E. Marriott, Gian Luca Di Tanna, Richard O. Day, and Jane E. Carland. "Voriconazole: an audit of hospital-based dosing and monitoring and evaluation of the predictive performance of a dose-prediction software package." Journal of Antimicrobial Chemotherapy 75, no. 7 (April 11, 2020): 1981–84. http://dx.doi.org/10.1093/jac/dkaa098.

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Abstract Background Therapeutic drug monitoring (TDM) is recommended to guide voriconazole therapy. Objectives To determine compliance of hospital-based voriconazole dosing and TDM with the Australian national guidelines and evaluate the predictive performance of a one-compartment population pharmacokinetic voriconazole model available in a commercial dose-prediction software package. Methods A retrospective audit of voriconazole therapy at an Australian public hospital (1 January to 31 December 2016) was undertaken. Data collected included patient demographics, dosing history and plasma concentrations. Concordance of dosing and TDM with Australian guidelines was assessed. Observed concentrations were compared with those predicted by dose-prediction software. Measures of bias (mean prediction error) and precision (mean squared prediction error) were calculated. Results Adherence to dosing guidelines for 110 courses of therapy (41% for prophylaxis and 59% for invasive fungal infections) was poor, unless oral formulation guidelines recommended a 200 mg dose, the most commonly prescribed dose (56% of prescriptions). Plasma voriconazole concentrations were obtained for 82% (90/110) of courses [median of 3 (range: 1–27) obtained per course]. A minority (27%) of plasma concentrations were trough concentrations [median concentration: 1.5 mg/L (range: <0.1 to >5.0 mg/L)]. Of trough concentrations, 57% (58/101) were therapeutic, 37% (37/101) were subtherapeutic and 6% (6/101) were supratherapeutic. The dose-prediction software performed well, with acceptable bias and precision of 0.09 mg/L (95% CI −0.08 to 0.27) and 1.32 (mg/L)2 (95% CI 0.96–1.67), respectively. Conclusions Voriconazole dosing was suboptimal based on published guidelines and TDM results. Dose-prediction software could enhance TDM-guided therapy.

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George, Alex, Bogdan R. Dinu, and Russell E. Ware. "Ndepth: Novel Dose Escalation to Predict Treatment with Hydroxyurea." Blood 126, no. 23 (December 3, 2015): 3419. http://dx.doi.org/10.1182/blood.v126.23.3419.3419.

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Abstract Several clinical trials have demonstrated that hydroxyurea therapy offers significant benefits for infants, children, and adolescents with sickle cell anemia. Patients on hydroxyurea who achieve a stable maximum tolerated dose (MTD), defined by a target level of mild marrow suppression, have greater laboratory and clinical benefits than those maintained on a lower dose. A complicating factor in achieving MTD is the significant inter-patient variability in MTD, but no way currently to predict the MTD for individual patients. As such, MTD is commonly achieved by gradual dose escalation in a resource-intensive process that often takes six to twelve months and delays optimal treatment benefits. Using published data from a cohort of previously untreated patients escalated to hydroxyurea MTD, we developed an equation to predict individualized MTD for patients initiating therapy. The primary objective of the Novel Dose Escalation to Predict Treatment with Hydroxyurea (NDEPTH, ClinicalTrials.gov 02042222) clinical trial is to determine the safety and efficacy of the dose-prediction equation. The study is designed as a prospective, open-label, randomized controlled trial consisting of two treatment arms: a Standard Treatment Arm utilizing a current published dose-escalation protocol for achieving hydroxyurea MTD and an Alternative Treatment Arm utilizing a dose-prediction equation to determine MTD, calculated prior to initiation of treatment. The primary endpoint of the study will be time to MTD for each arm. Additional endpoints include analyses of safety and biological responses to hydroxyurea therapy At the planned interim analysis of this study, we have recruited ten participants to each arm of the study. Kaplan-Meier analysis of these twenty participants indicates that there is a trend approaching significance (p = 0.071) for subjects on the dose-prediction arm to reach MTD faster than those on the dose-escalation arm, with a median time to MTD of 112 days versus 309 days respectively. Additionally, the dose-prediction equation has a high degree of accuracy (R2 = 0.66; p = 0.001) in predicting the actual MTD for all enrolled participants, with a mean predicted dose of 26.4 ± 1.6 mg/kg and actual dose of 27.1 ± 4.1 mg/kg. Children on the dose-prediction Alternative Treatment Arm have had a higher incidence of excessive myelosuppression requiring temporary dose cessation in three subjects, but no clinical adverse events. These interim results suggest that the dose-prediction equation is safe and effective in determining MTD for young patients with SCA initiating hydroxyurea therapy. Final analysis of the safety and efficacy of the dose-prediction equation will be performed upon completion of the study. Disclosures Off Label Use: This abstract describes the use of hydroxyurea in pediatric sickle cell patients. Hydroxyurea is not currently approved by the FDA for this purpose.. Ware:Bayer Pharmaceuticals: Consultancy; Eli Lilly: Other: DSMB membership; Bristol Myers Squibb: Research Funding; Biomedomics: Research Funding.

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Sharabiani, Ashkan, Edith A. Nutescu, William L. Galanter, and Houshang Darabi. "A New Approach towards Minimizing the Risk of Misdosing Warfarin Initiation Doses." Computational and Mathematical Methods in Medicine 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/5340845.

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It is a challenge to be able to prescribe the optimal initial dose of warfarin. There have been many studies focused on an efficient strategy to determine the optimal initial dose. Numerous clinical, genetic, and environmental factors affect the warfarin dose response. In practice, it is common that the initial warfarin dose is substantially different from the stable maintenance dose, which may increase the risk of bleeding or thrombosis prior to achieving the stable maintenance dose. In order to minimize the risk of misdosing, despite popular warfarin dose prediction models in the literature which create dose predictions solely based on patients’ attributes, we have taken physicians’ opinions towards the initial dose into consideration. The initial doses selected by clinicians, along with other standard clinical factors, are used to determine an estimate of the difference between the initial dose and estimated maintenance dose using shrinkage methods. The selected shrinkage method was LASSO (Least Absolute Shrinkage and Selection Operator). The estimated maintenance dose was more accurate than the original initial dose, the dose predicted by a linear model without involving the clinicians initial dose, and the values predicted by the most commonly used model in the literature, the Gage clinical model.

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Caldwell, M. D., R. L. Berg, K. Q. Zhang, I. Glurich, J. R. Schmelzer, S. H. Yale, H. J. Vidaillet, and J. K. Burmester. "Evaluation of Genetic Factors for Warfarin Dose Prediction." Clinical Medicine & Research 5, no. 1 (March 1, 2007): 8–16. http://dx.doi.org/10.3121/cmr.2007.724.

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Eriksson, Niclas, and Mia Wadelius. "Prediction of warfarin dose: why, when and how?" Pharmacogenomics 13, no. 4 (March 2012): 429–40. http://dx.doi.org/10.2217/pgs.11.184.

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Altmann, Vivian, Mariana Rieck, Artur Schumacher-Schuh, Sídia Callegari-Jacques, Carlos de Mello Rieder, and Mara Hutz. "Pharmacogenetics of levodopa: An algorithm for dose prediction." Parkinsonism & Related Disorders 22 (January 2016): e89. http://dx.doi.org/10.1016/j.parkreldis.2015.10.184.

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Altmann, Vivian, Mariana Rieck, Artur Schumacher-Schuh, Sídia Callegari-Jacques, Carlos de Mello Rieder, and Mara Hutz. "Pharmacogenetics of levodopa: An algorithm for dose prediction." Parkinsonism & Related Disorders 22 (January 2016): e16. http://dx.doi.org/10.1016/j.parkreldis.2015.10.534.

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Biss, Tina, Anna-Karin Hamberg, Peter Avery, Mia Wadelius, and Farhad Kamali. "Warfarin dose prediction in children using pharmacogenetics information." British Journal of Haematology 159, no. 1 (July 18, 2012): 106–9. http://dx.doi.org/10.1111/j.1365-2141.2012.09230.x.

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Sandgren, David J., Charles A. Salter, Ira H. Levine, James A. Ross, Patricia K. Lillis-Hearne, and William F. Blakely. "Biodosimetry Assessment Tool (BAT) Software—Dose Prediction Algorithms." Health Physics 99 (November 2010): S171—S183. http://dx.doi.org/10.1097/hp.0b013e3181f0fe6c.

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Fiorino, C., G. Rizzo, S. Broggi, G. M. Cattaneo, E. Maggiulli, E. Scalco, G. Sanguineti, and R. Calandrino. "507 speaker IMAGE-BASED DOSE-VOLUME EFFECTS PREDICTION." Radiotherapy and Oncology 99 (May 2011): S205—S206. http://dx.doi.org/10.1016/s0167-8140(11)70629-6.

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Andreev, S. G., and Y. A. Eidelman. "Dose-response prediction for radiation-induced chromosomal instability." Radiation Protection Dosimetry 143, no. 2-4 (December 23, 2010): 270–73. http://dx.doi.org/10.1093/rpd/ncq509.

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van Elmpt, W. J. C., S. M. J. J. G. Nijsten, B. J. Mijnheer, and A. W. H. Minken. "Experimental verification of a portal dose prediction model." Medical Physics 32, no. 9 (August 22, 2005): 2805–18. http://dx.doi.org/10.1118/1.1987988.

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Sohrabi, Mohammad Karim, and Alireza Tajik. "Multi-objective feature selection for warfarin dose prediction." Computational Biology and Chemistry 69 (August 2017): 126–33. http://dx.doi.org/10.1016/j.compbiolchem.2017.06.002.

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Gervasoni, N. "Lithium dose prediction based on 24 hours single dose levels: a prospective evaluation." Pharmacological Research 48, no. 6 (December 2003): 649–53. http://dx.doi.org/10.1016/s1043-6618(03)00220-2.

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Gaikwad, Tejasvita, Kanjaksha Ghosh, Peter Avery, Farhad Kamali, and Shrimati Shetty. "Warfarin Dose Model for the Prediction of Stable Maintenance Dose in Indian Patients." Clinical and Applied Thrombosis/Hemostasis 24, no. 2 (January 4, 2017): 353–59. http://dx.doi.org/10.1177/1076029616683046.

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Bertschy, G., S. Vandel, B. Vandel, G. Allers, P. Bechtel, and R. Volmat. "Desipramine Dose Prediction Based on 24-Hour Single-Dose Levels: Feasibility and Validity." Pharmacopsychiatry 22, no. 04 (July 1989): 161–64. http://dx.doi.org/10.1055/s-2007-1014600.

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