Virtual Clinical Trials Research Articles

Optimized generation of high resolution breast anthropomorphic software phantoms

The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin. Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 μm)(3)∕voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm). A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.

In Vitro and In Vivo Metabolism and Pharmacokinetics of BMS-562086, a Potent and Orally Bioavailable Corticotropin-Releasing Factor-1 Receptor Antagonist

The absorption, distribution, metabolism, and excretion (ADME) and the pharmacokinetic characteristics of BMS-562086 [pexacerfont; 8-(6-methoxy-2-methyl-3-pyridinyl)-2,7-dimethyl-N-[(1R)-1-methylpropyl]pyrazolo(1,5-a)-1,3,5-triazin-4-amine (DPC-A69448)] were investigated in vitro and in animals to support its clinical development. BMS-562086 was orally bioavailable in rats, dogs, and chimpanzees, with an absolute oral bioavailability of 40.1, 58.8, and 58.5%, respectively. BMS-562086 was extensively metabolized in hepatocytes from all species and completely metabolized in rats. The primary biotransformation pathways found for BMS-562086 in both liver microsomal and hepatocyte preparations and in rats were similar. These included O-demethylation, hydroxylation at the N-alkyl side chain and N-dealkylation. Multiple cytochromes P450 including CYP3A4/5 were involved in the metabolic clearance of BMS-562086. Both renal and biliary excretion played a significant role in elimination of the metabolites of BMS-562086. The involvement of other metabolic enzymes in addition to CYP3A4/5 in elimination of BMS-562086 suggests a reduced potential for drug-drug interaction through modulation of CYP3A4/5. Chimpanzees proved to be a good animal model in predicting BMS-562086 human clearance. Virtual clinical trials performed with a population-based ADME simulator suggested that a minimal dose of 100 mg daily would provide sufficient drug exposure to achieve plasma concentrations above the projected human efficacious plasma concentration of BMS-562086 (> 500 nM). In summary, BMS-562086 exhibited favorable ADME and pharmacokinetic properties for further development.

Pharmacodynamic modelling of biomarker data in oncology.

The development of pharmacodynamic (PD) biomarkers in oncology has implications for design of clinical protocols from preclinical data and for predicting clinical outcomes from early clinical data. Two classes of biomarkers have received particular attention. Phosphoproteins in biopsy samples are markers of inhibition of signalling pathways, target sites for many novel agents. Biomarkers of apoptosis in plasma can measure tumour cell killing by drugs in phase I clinical trials. The predictive power of PD biomarkers is enhanced by data modelling. With pharmacokinetic models, PD models form PK/PD models that predict the time course both of drug concentration and drug effects. If biomarkers of drug toxicity are also measured, the models can predict drug selectivity as well as efficacy. PK/PD models, in conjunction with disease models, make possible virtual clinical trials, in which multiple trial designs are assessed in silico, so the optimal trial design can be selected for experimental evaluation.

Pharmacogenomics of Codeine, Morphine, and Morphine-6-Glucuronide

Background and Objective: The analgesic effect of codeine depends on the formation of the opioid metabolites morphine and morphine-6-glucuronide. Different factors have been shown or suspected to affect the safety and efficacy of codeine treatment. The objective of the current study is to assess and quantify the impact of important pharmacokinetic factors, using a mechanistic modeling approach. Methods: By means of a generic modeling approach integrating prior physiologic knowledge, we systematically investigated the complex dependence of opioid exposure on cytochrome P450 2D6 and 3A4 (CYP2D6 and CYP3A4), and uridine diphosphate glucuronosyltransferase 2B7 (UGT2B7) activity, as well as renal function, by means of a virtual clinical trial. Results: First, the known dominant role of CYP2D6 activity for morphine exposure was reproduced. Second, the model demonstrated that mild and moderate renal impairment and co-administration of CYP3A4 inhibitors have only minor influences on opioid exposure. Third, the model showed — in contrast to current opinion — that increased UGT2B7 activity is associated with a decrease in active opioid exposure. Conclusion: Overall, the model-based analysis predicts a wide range of morphine levels after codeine administration and supports recent doubts about safe and efficacious use of codeine for analgesia in non-genotyped individuals.

Use of a digitally reconstructed radiograph-based computer simulation for the optimisation of chest radiographic techniques for computed radiography imaging systems.

The purpose of this study was to derive an optimum radiographic technique for computed radiography (CR) chest imaging using a digitally reconstructed radiograph computer simulator. The simulator is capable of producing CR chest radiographs of adults with various tube potentials, receptor doses and scatter rejection. Four experienced image evaluators graded images of average and obese adult patients at different potentials (average-sized, n=50; obese, n=20), receptor doses (n=10) and scatter rejection techniques (average-sized, n=20; obese, n=20). The quality of the images was evaluated using visually graded analysis. The influence of rib contrast was also assessed. For average-sized patients, image quality improved when tube potential was reduced compared with the reference (102 kVp). No scatter rejection was indicated. For obese patients, it has been shown that an antiscatter grid is indicated, and should be used in conjunction with as low a tube potential as possible (while allowing exposure times <20 ms). It is also possible to reduce receptor air kerma by 50% without adversely influencing image quality. Rib contrast did not interfere at any tube potential. A virtual clinical trial has been performed with simulated chest CR images. Results indicate that low tube potentials (<102 kVp) are optimal for average and obese adults, the former acquired without scatter rejection, the latter with an anti-scatter grid. Lower receptor (and therefore patient doses) than those used clinically are possible while maintaining adequate image quality.

Improving Safety of Glucose Control in Intensive Care using Virtual Patients and Simulated Clinical Trials

Despite the potential clinical benefits of normalizing blood glucose in critically ill patients, the risk of hypoglycemia is a major barrier to widespread clinical adoption of accurate glycemic control. To compare five glucose control protocols, a validated insulin‐glucose system model was employed to perform simulated clinical trials. STAR, SPRINT, UNC, Yale and Glucontrol protocols were assessed over a medical‐surgical intensive care unit patient cohort. Results were interpreted separately for patients with low to high sensitivity to insulin, and low to high variability in metabolic state. STAR and SPRINT provided good glucose control with risk of severe hypoglycemia less than 0.05% across all patient groups. UNC also achieved good control for patients with low and medium levels of insulin sensitivity (SI), but risk of severe hypoglycemia was raised for patients with high SI. Glucontrol showed degradation of performance for patients with high metabolic variability.

Virtual HDR SBRT For Localized Prostatic Carcinoma: Efficacy And Quality Of Life Assessment

Virtual HDR SBRT For Localized Prostatic Carcinoma: Efficacy And Quality Of Life Assessment

Dose adaptation of capecitabine based on individual prediction of limiting toxicity grade: evaluation by clinical trial simulation

Anticancer drugs often show a narrow therapeutic index and high inter-patient variability, which can lead to the need to adjust doses individually during the treatment. One approach to doing this is to use individual model predictions. Such methods have been proposed to target-specific drug concentrations or blood cell count, both of which are continuous variables. However, many toxic effects are evaluated on a categorical scale. This article presents a novel approach to dose adjustments for reducing a graded toxicity while maintaining efficacy, applied to hand-and-foot syndrome (HFS) induced by capecitabine. A mixed-effects proportional odds Markov model relating capecitabine doses to HFS grades was individually adjusted at the end of each treatment cycle (3weeks) by estimating subject-specific parameters by Bayesian MAP technique. It was then used to predict the risk of intolerable (grade≥2) toxicity over the next treatment cycle and determine the next dose accordingly, targeting a predefined tolerable risk. Proof of concept was given by simulating virtual clinical trials, where the standard dose reductions and the prediction-based adaptations were compared, and where the therapeutic effect was simulated using a colorectal tumor inhibition model. A sensitivity analysis was carried out to test various specifications of prediction-based adaptation. Individualized dose adaptation might reduce the average duration of intolerable HFS by 10days as compared to the standard reductions (3.8weeks vs. 5.2weeks; 27% relative reduction) without compromising antitumor efficacy (both responder rates were 49%). A clinical trial comparing the two methods should include 350 patients per arm to achieve at least 90% power to show a difference in grade≥2 HFS duration at an alpha level of 0.05. These results indicate that individual prediction-based dose adaptation based on ordinal data may be feasible and beneficial.

SU‐E‐T‐32: An Integrated IGART Planning Environment

Purpose: Open Radiological Archiving and Communication System(ORACS) relational research database, which stores images, projections, plans, trials and doses etc, is the backbone for Image Guided Adaptive Radiation Therapy (IGART) Research Project at VCU. Philips Pinnacle3 is being used by this project for IGART adaptive planning of Virtual Clinical Trials (VCT). Therefore, in order to establish a user‐friendly interactive environment and facilitate collaboration between different sub‐projects, seamless integration of these two systems will be a must. Methods: It is well‐known that Pinnacle3 lacks the capacity to interact with a database server. However, we take advantage of Pinnacleˈs configurability and scripting power to enhance Pinnacle with new GUI controls, which invoke a middleware layer to access ORACS database. Users can import images from ORACS database through remote web services or locally, and save contours and plans and doses and other derived Pinnacle data back to ORACS database by simply clicking these new GUI controls. Moreover, a Pinnacle database can be re‐created easily and entirely from ORACS database through these new GUI controls and the middleware layer. The same technique can be used to create connection between Pinnacle3 and other database servers. Also because ORACS database uses web services for data access, if a specific middleware is created, any Treatment Planning System can access ORACS database interactively. Results: We presented a software infrastructure for seamless integration of ORACS research database and Philips Pinnacle3, which created an integrated virtual clinical trial environment for IGART researchers at our institution. Conclusions: Contemporary IGART research imposes new demands on the IT infrastructure of Radiation Oncology departments. We created a data management system which takes first steps towards managing new and complex tasks of automated analysis of large data sets.NIH Grant P01 CA11602

Monte Carlo simulations: maximizing antibiotic pharmacokinetic data to optimize clinical practice for critically ill patients

Infections in critically ill patients continue to result in unacceptably high morbidity and mortality. Although few data exist for correlating antibiotic exposure with outcome, antibiotic dosing is likely to be highly important for maximizing resolution of infection in many patients. The practical and financial difficulties of performing pharmacokinetic (PK) studies in critically ill patients mean that analyses to maximize data such as Monte Carlo simulation (MCS) are highly valuable. MCS uses computer software to perform virtual clinical trials. The building blocks for MCS are: firstly, a robust population PK model from the patient population of interest; secondly, descriptors of the effect of covariates that influence the PK parameters; thirdly, description of the susceptibility of bacteria to the antibiotic and finally a PK/pharmacodynamic (PD) target associated with antibiotic efficacy. Probability of target attainment (PTA) outputs can then be generated that describe the proportion of patients that will achieve a pre-specified PD target for an MIC distribution. Such analyses can then inform dosing requirements, which can be used to have a high likelihood of achieving PK/PD targets for organisms with different MICs. In this issue of JAC, Zelenitsky et al. provide a very useful example of MCS for interpreting the optimal methods for dosing meropenem, piperacillin/tazobactam, cefepime and ceftobiprole in critically ill patients.

From the Evolution of Toxin Resistance to Virtual Clinical Trials: The Role of Mathematical Models in Oncology

Sustainable delivery of quality healthcare at affordable cost is a major challenge, especially in oncology. Through interdisciplinary collaboration, researchers can provide new insights into familiar concepts and radically change the ways in which biopharmaceutical and medical studies are conducted and translated into clinical practice. One interdisciplinary approach is 'Virtual R&D', that is, biomedical research and development aided by mathematical models of the human body. This approach has facilitated the development of the 'virtual patient', a clinically validated modeling system that accurately predicts efficacy and toxicity of various oncology drug combinations in individuals and in populations. The use of virtual patients in clinical research will dramatically shorten the period of development of new drugs. Such models can also be used to personalize treatment regimens, substantially reducing the risk of clinical failure. This strategy can improve the quality of healthcare and reduce costs associated with clinical development.

Use of Virtual Patient Populations for Rescuing Discontinued Drug Candidates and for Reducing the Number of Patients in Clinical Trials

The decreasing cost-efficiency of drug development threatens to result in a severe shortage of innovative drugs, which may seriously compromise patient healthcare. This risk underlines the urgency to change the paradigm in clinical research. Here, we examine a novel concept of conducting virtual clinical trials for efficiently screening drug candidates, and for evaluating their prospects of being brought to the market successfully. The virtual clinical trials are carried out by using virtual patients (denoted Optimata Virtual Patients -- OVPs). The OVP, a set of mathematical algorithms that describe the main pathological and physiological dynamic processes affected by the administered drug, has been shown to accurately predict docetaxel efficacy and safety in individual breast cancer patients. We report a test case in which virtual clinical trials have been conducted by using OVP populations for rescuing a discontinued oncology compound, ISIS-5132 (ISIS Pharmaceuticals Inc.). Our in silico study suggested that ISIS-5132 may be efficacious in combination with another drug, sunitinib malate (Sutent, Pfizer Inc.), for the treatment of prostate cancer. The recommended combined treatment is predicted to result in a higher five-year Progression-Free Survival than monotherapy with either drug alone.

Differences in Reactivation of Tuberculosis Induced from Anti-TNF Treatments Are Based on Bioavailability in Granulomatous Tissue

The immune response to Mycobacterium tuberculosis (Mtb) infection is complex. Experimental evidence has revealed that tumor necrosis factor (TNF) plays a major role in host defense against Mtb in both active and latent phases of infection. TNF-neutralizing drugs used to treat inflammatory disorders have been reported to increase the risk of tuberculosis (TB), in accordance with animal studies. The present study takes a computational approach toward characterizing the role of TNF in protection against the tubercle bacillus in both active and latent infection. We extend our previous mathematical models to investigate the roles and production of soluble (sTNF) and transmembrane TNF (tmTNF). We analyze effects of anti-TNF therapy in virtual clinical trials (VCTs) by simulating two of the most commonly used therapies, anti-TNF antibody and TNF receptor fusion, predicting mechanisms that explain observed differences in TB reactivation rates. The major findings from this study are that bioavailability of TNF following anti-TNF therapy is the primary factor for causing reactivation of latent infection and that sTNF—even at very low levels—is essential for control of infection. Using a mathematical model, it is possible to distinguish mechanisms of action of the anti-TNF treatments and gain insights into the role of TNF in TB control and pathology. Our study suggests that a TNF-modulating agent could be developed that could balance the requirement for reduction of inflammation with the necessity to maintain resistance to infection and microbial diseases. Alternatively, the dose and timing of anti-TNF therapy could be modified. Anti-TNF therapy will likely lead to numerous incidents of primary TB if used in areas where exposure is likely.

Treating Alcoholics with a Co-Occurring Anxiety Disorder

Objectives: Psychiatric comorbidity likely complicates the treatment of alcoholism. Studies of alcoholics with and without co-occurring anxiety disorders suggest that the presence of an anxiety disorder can significantly impact alcoholism treatment efficacy and can increase subsequent alcohol relapse rates. The objective of this study was to estimate the long-term relapse rates and costs associated with treating an alcoholic with an anxiety disorder. Methods: We have used a Markov model to create a virtual clinical trial to predict and compare long-term outcomes in alcoholics with and without a co-occurring anxiety disorder. We have also incorporated the subsequent associated costs of the two subject groups. Results: In our model, at the end of five years, 34% of our hypothetical group of 100 alcoholics with an ameliorated anxiety disorder remained abstinent compared to only 8% of the 100 alcoholics with an untreated anxiety disorder. Furthermore, the costs associated with these relapses are greater for the untreated group, after factoring in the costs of anxiety treatment in the treated group, even if only 50% of the anxiety treated group gets amelioration of their anxiety disorder. Conclusion: Our results suggest that addressing co-occurring anxiety disorders in an alcohol treatment setting may have a dramatic impact on alcohol relapse and its associated costs.