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Background: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. Results: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. Conclusions: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances. [ABSTRACT FROM AUTHOR]

Mycophenolate mofetil (MMF) is a key component of postgrafting immunosuppression in hematopoietic cell transplant (HCT) recipients. The plasma area under the curve (AUC) of its active metabolite, mycophenolic acid (MPA), is associated with MMF efficacy and toxicity. This study developed a population pharmacokinetic model of MPA in HCT recipients and created limited sampling schedules (LSSs) to enable individualized pharmacotherapy. A retrospective evaluation of MPA concentration-time data following a 2-hour MMF intravenous (IV) infusion was conducted in 77 HCT recipients. The final model consisted of 1 and 2 compartments for MMF and MPA pharmacokinetics, respectively. The mean estimated values (coefficient of variation, %) for total systemic clearance, distributional clearance, and central and peripheral compartment volumes of MPA were 36.9 L/h (34.5%), 15.3 L/h (80.4%), 11.9 L (71.7%), and 182 L (127%), respectively. No covariates significantly explained variability among individuals. Optimal LSSs were derived using a simulation approach based on the scaled mean squared error. A 5-sample schedule of 2, 2.5, 3, 5, and 6 hours from the start of the infusion precisely estimated MPA AUC0-12 h for Q12-hour IV MMF. A comparable schedule (2, 2.5, 3, 4, and 6 hours) similarly estimated MPA AUC0-8h for Q8-hour dosing. [ABSTRACT FROM PUBLISHER]

We determined the postoperative pharmacokinetics (PK), safety, and analgesic effects of ketorolac in 14 infants (aged <6 months) receiving a single intravenous (IV) administration of racemic ketorolac or placebo. Information on the PK of ketorolac in infants is limited. Unblinded studies suggest ketorolac may be useful in infants. This double-blinded, placebo-controlled study enrolled 14 infants (aged <6 months) postoperatively. At 6-18 h after surgery, infants were randomized to receive placebo, 0.5 mg·kg, or 1 mg·kg ketorolac IV. All infants received morphine sulfate as needed for pain control. Blood was collected up to 12-h postdosing. Analysis used noncompartmental and compartmental population modeling methods. In addition to noncompartmental and empirical Bayes PK modeling, data were integrated with a previously studied data set comprising 25 infants and toddlers (aged 6-18 months). A two-compartmental model described the comprehensive data set. The population estimates of the R (+) isomer were (%CV): central volume of distribution 1130 (10%) ml, peripheral volume of distribution 626 (25%) ml, and clearance from the central compartment 7.40 (8%) ml·min. Those of the S (−) isomer were 1930 (15%) ml, 319 (58%) ml, and 39.5 (13%) ml·min. Typical elimination half-lives were 191 and 33 min, respectively. There was a trend for increased clearance and central volume with increasing age and weight. The base model suggested that clearance of the S (−) isomer was weakly related to age; however, when body size adjustment was added to the model, no covariates were significant. Safety assessment showed no changes in renal or hepatic function tests, surgical drain output, or continuous oximetry between groups. Cumulative morphine administration showed large inter-patient variability and was not different between groups. Stereo-isomer-specific clearance of ketorolac in infants (aged 2-6 months) shows rapid elimination of the analgesic S (−) isomer as reported in infants aged 6-18 months. No adverse effects were seen after a single IV ketorolac dose. [ABSTRACT FROM AUTHOR]

We describe a method to measure protein synthesis and catabolism in humans without prior purification and use the method to measure the turnover of sutlactant protein-B (SP-B). SP-B, a lung-specific, hydrophobic protein essential for fetal-neonatal respiratoty transition, is present in only picomolar quantities in tracheal aspirate samples and difficult to isolate for dynamic turnover studies using traditional in vivo tracer techniques. Using infusion of [5,5,5-2H3] leucine and a targeted proteomics method, we measured both the quantity and kinetics of SP-B tryptic peptides in tracheal aspirate samples of symptomatic newborn infants. The fractional synthetic rate (FSR) of SP-B measured using the most abundant proteolytic fragment, a 10 amino acid peptide from the carboxy-terminus of proSP-B (SPTGEWLPR), from the circulating leucine pool was 0.035 ± 0.005 h-1, and the fractional catabolic rate was 0.044 ± 0.003 h-1. This technique permits high-throughput and sensitive measurement of turnover of low abundance proteins with minimal sample preparation. [ABSTRACT FROM AUTHOR]

A new approach for objectively analyzing the aggregation of acetylcholine receptors (AChRs) through power spectrum analysis derived from fast Fourier transform (FFT) of images has been developed. Presently, detection of AChR aggregates at neuromuscular junctions is not easily accomplished. Though the formation of AChR clusters results in periodic gray-level variations that differ with time, no study reporting their correlation with frequency information in the Fourier domain for aggregates’ detection purposes exists. To this end, we processed time-lapse images of AChR aggregates’ formation on murine myotubes to extract peak values of power spectra. To validate interpretation of the Fourier spectra analysis, a computer routine was developed to semi-automatically count AChR aggregates. We found: (1) logarithmic maxima of Fourier spectra correlated significantly with experimentation time; (2) cluster count correlated significantly with time only after clusters were discernable from images, signifying that this method heavily depended on definitive growth data and thresholding values; (3) exponents of Fourier maxima versus time and cluster count versus time profiles during this phase compared favorably, indicating that both methods were analyzing identical cluster growth rates. Our observations suggest that analysis via FFT power spectrum is sensitive and robust enough to automatically quantify AChR aggregates. [Copyright &y& Elsevier]

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies., (© 2019 John Wiley & Sons, Ltd.)

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing., (© 2018 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune-cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment., Competing Interests: C.A.S.-M. receives research support from Pfizer and MedImmune and serves on the advisory board for Polyphor. I.H.B., P.V., B.W., R.N. and L.R. were employees of MedImmune. A.S.P. receives research support from MedImmune. Other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Interleukin (IL)-13 is involved in the pathogenesis of some types of asthma. Tralokinumab is a human immunoglobulin G 4 monoclonal antibody that specifically binds to IL-13. Two placebo-controlled phase II studies (phase IIa, NCT00873860 and phase IIb, NCT01402986) have been conducted in which tralokinumab was administered subcutaneously. This investigation aimed to characterize tralokinumab's dose-exposure-response (forced expiratory volume in 1 s (FEV 1 )) relationship in patients with asthma and to predict the most appropriate dose for phase III. An integrated population pharmacokinetic-pharmacodynamic (PK/PD) modeling analysis was required for phase III dose selection, due to differing phase II patient populations, designs, and regimens. Analysis of combined datasets enabled the identification of tralokinumab's dose-exposure-FEV 1 response relationship in patients with asthma. Near-maximal FEV 1 increase was predicted at a dose of 300 mg SC once every 2 weeks (Q2W). This dose was chosen for tralokinumab in the phase III clinical development program for treatment of severe, uncontrolled asthma., (© 2017 American Society for Clinical Pharmacology and Therapeutics.)

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

Purpose: Quantitative relationships between 9-β-D-arabinofuranosyl-2-fluoroadenine (F-ara-A) concentrations and lymphosuppression have not been reported, but would be useful for regimen design. A population pharmacokinetic/pharmacodynamic model was constructed in this study using data from 41 hematopoietic cell transplant (HCT) recipients conditioned with busulfan in combination with fludarabine (total dose 120 mg/m², Protocol 1519) or with fludarabine (total dose 250 mg/m²) with rabbit antithymocyte globulin (rATG, Protocol 2041)., Methods: Individual pharmacokinetic parameters were fixed to post hoc Bayesian estimates, and circulating absolute lymphocyte counts (ALC) were obtained during the 3 weeks prior to graft infusion. A semi-physiological cell-kill model with three lymphocyte transit compartments was applied and aptly characterized the time course of suppression of circulating ALC by fludarabine administration. Drug- and system-specific parameters were estimated using a maximum likelihood expectation maximization algorithm, and the final model was qualified using an internal visual predictive check., Results: The final model successfully characterized the time course and variability in ALC. Pharmacodynamic parameters exhibited considerable between subject variability (38.9-211 %). The HCT protocol was the only covariate associated with the pharmacodynamic parameters, specifically the lymphocyte kill rate, the transit rate between lymphocyte compartments, and the baseline ALC., Conclusions: This model can be used to simulate the degree of lymphosuppression for design of future fludarabine-based conditioning regimens.

A key role of B cells in the mammalian immune response is the generation of antibodies that serve to protect the organism against antigenic challenges. The same process may also be detrimental in the context of autoimmunity. Several modeling approaches have been applied to this aspect of the immune response, from predicting potential epitopes to describing B cells progress through developmental models and simulating antibody production. Here we review some of the modeling techniques, and summarize models that describe different activation mechanisms for B cells, including T cell dependent and independent models. We focus on viral infection as a prototype system, and briefly describe case studies in other disease areas such as bacterial infection and oncology. We single out aspects of the B cell response for which there are current knowledge gaps. We outline areas in need of further research in modeling applications to ultimately produce a "B cell module" for a complete immune response model.

A mathematical pharmacokinetic/anti-drug-antibody (PK/ADA) model was constructed for quantitatively assessing immunogenicity for therapeutic proteins. The model is inspired by traditional pharmacokinetic/pharmacodynamic (PK/PD) models, and is based on the observed impact of ADA on protein drug clearance. The hypothesis for this work is that altered drug PK contains information about the extent and timing of ADA generation. By fitting drug PK profiles while accounting for ADA-mediated drug clearance, the model provides an approach to characterize ADA generation during the study, including the maximum ADA response, sensitivity of ADA response to drug dose level, affinity maturation rate, time lag to observe an ADA response, and the elimination rate for ADA-drug complex. The model also provides a mean to estimate putative concentration-time profiles for ADA, ADA-drug complex, and ADA binding affinity-time profile. When simulating ADA responses to various drug dose levels, bell-shaped dose-response curves were generated. The model contains simultaneous quantitative modeling and provides estimation of the characteristics of therapeutic protein drug PK and ADA responses in vivo. With further experimental validation, the model may be applied to the simulation of ADA response to therapeutic protein drugs in silico, or be applied in subsequent PK/PD models.

The industry-wide biopharmaceutical (i.e., biologic, biotherapeutic) pipeline has been growing at an astonishing rate over the last decade with the proportion of approved new biological entities to new chemical entities on the rise. As biopharmaceuticals appear to be growing in complexity in terms of their structure and mechanism of action, so are interpretation, analysis, and prediction of their quantitative pharmacology. We present here a modeling and simulation (M&S) framework for the successful preclinical development of monoclonal antibodies (as an illustrative example of biopharmaceuticals) and discuss M&S strategies for its implementation. Critical activities during early discovery, lead optimization, and the selection of starting doses for the first-in-human study are discussed in the context of pharmacokinetic-pharmacodynamic (PKPD) and M&S. It was shown that these stages of preclinical development are and should be reliant on M&S activities including systems biology (SB), systems pharmacology (SP), and translational pharmacology (TP). SB, SP, and TP provide an integrated and rationalized framework for decision making during the preclinical development phase. In addition, they provide increased target and systems understanding, describe and interpret data generated in vitro and in vivo, predict human PKPD, and provide a rationalized approach to designing the first-in-human study.

Physiological changes during pregnancy may alter drug pharmacokinetics. Therefore, mechanistic understanding of these changes and, ultimately, clinical studies in pregnant women are necessary to determine if and how dosing regimens should be adjusted. Because of the typically limited number of patients who can be recruited in this patient group, efficient design and analysis of these studies is of special relevance. This paper is a summary of a conference session organized at the American Conference of Pharmacometrics in April 2011, around the topic of applying pharmacometric methodology to this important problem. The discussion included both design and analysis of clinical studies during pregnancy and in silico predictions. An overview of different pharmacometric methods relevant to this subject was given. The impact of pharmacometrics was illustrated using a range of case examples of studies around pregnancy., (© 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.)

Therapeutic drug monitoring of daily intravenous (IV) busulfan currently requires hospital admission. Population pharmacokinetic modeling and determination of an optimal pharmacokinetic sampling schedule over 6 hours could allow for personalizing these busulfan doses in the outpatient clinic. A retrospective evaluation of daily IV busulfan pharmacokinetics was conducted in 37 adults. SPK and NONMEM software were used to estimate the population pharmacokinetic parameters. Subsequent to model building, the area under the concentration-time curve (AUC) was computed using NONMEM. A 1-compartment model best fit the data. The optimal 6-hour outpatient sampling schedule was constructed using a simulation approach that sought to minimize scaled mean squared error for the clearance and volume parameters for each simulated individual. The best sampling times were 2.75, 3, 3.25, 5.5, 5.75, and 6 hours from the start of a 3-hour infusion. With these sampling times, the maximum a posteriori (MAP) Bayesian estimation was superior to maximum likelihood estimation with more samples. An individual patient's busulfan AUC and pharmacokinetic parameters may be accurately estimated with an outpatient sampling schedule that is used in conjunction with MAP Bayesian estimation, with a parameter prior based on population pharmacokinetic modeling. Prospective validation of this approach is needed.

Nurses caring for traumatic brain injury (TBI) patients with intracranial hypertension (ICH) recognize that patients whose intracranial adaptive capacity is reduced are susceptible to periods of disproportionate increase in intracranial pressure (DIICP) in response to a variety of stimuli. It is possible that DIICP signals potential secondary brain damage due to sustained or intermittent ICH. However, there are few clinically accessible intracranial pressure (ICP) measurement parameters that allow nurses and other critical care clinicians to identify patients at risk of DIICP. The purpose of this study was to investigate whether there are specific minute-to-minute trends in ICP variability during the first 48 hr of monitoring that might accurately predict DIICP in patients with severe TBI. A total of 38 patients with severe TBI were sampled from the data set of a randomized controlled trial testing bedside monitoring displays and cerebral perfusion pressure management in individuals with TBI or sub-arachnoid hemorrhage. The investigators retrospectively examined the rates of change (slope) in mean, standard deviation, and variance of ICP on a 1-min basis for 30 consecutive min prior to a specified DIICP event. There was a significantly increasing linear and quadratic slope in mean ICP prior to the development of DIICP, compared with the comparison data set (p < .05). It is feasible to display moving averages in modern bedside monitoring. Such an arrangement may be useful to provide visual displays that provide immediate clinically relevant information regarding the patients with decreased adaptive capacity and therefore increased risk of DIICP.

In this paper, we examined the added value of fuzzy nonlinear regression to identify individual pharmacokinetic parameters in the case of noisy fuzzy data and/or small sample sizes. We first described three approaches that use least squares of errors as a fitting criterion for parameter estimation by fuzzy regression. Next, we compared the estimation and prediction capability of fuzzy least squares (FLS) and ordinary least squares (OLS) regressions via a simulation experiment, so as to determine the conditions of data size and variability under which one approach could be deemed superior over the other. We considered two empirical pharmacokinetic models. Our results showed that OLS regression outperformed FLS regression when the sample size was larger and/or there existed more outliers in the data. Overall, FLS regression was more powerful as the dataset size decreased. When the data were smaller in size and contained more variability, FLS regression's performance remained better than that of the OLS regression. Although the accuracy of the three FLS regression approaches was very close in almost all instances, those that estimated fuzzy parameters were superior in terms of predictive capability. These findings could aid in selecting the proper regression technique to employ in the presence of fuzzy data.

Context: Understanding the role of insulin resistance in type 1 diabetes may lead to new prevention strategies. Estimates of insulin resistance in first-degree relatives of those with type 1 diabetes may be obtained using the minimal model of glucose kinetics incorporating a population approach., Objective: The objective of the study was to explore parameters contributing to glucose homeostasis in a cross-sectional study of first-degree relatives across a wide age range., Design: Insulin sensitivity (SI) was assessed using the minimal model of glucose kinetics after an oral glucose tolerance test combined with nonlinear mixed-effects modeling. Beta-cell function was measured from the insulinogenic index at 30 min (IGI(30)). Disposition index (DI) was estimated as the product of SI and IGI(30)., Setting: The study was conducted at an academic center., Subjects: Subjects included 1241 first-degree relatives (aged 2-75 yr)., Results: SI was found to be negatively correlated with age, whereas IGI(30) increased until young adulthood. The increase IGI(30) was apparently insufficient to compensate for the insulin resistance because DI decreased linearly at the rate of 0.035 (10(-2) min(-1) mmol(-1) liter per year) after young adulthood. Both IGI(30) and DI were significantly lower in those with vs. without autoantibodies, whereas there was no difference between these groups with respect to SI., Conclusions: Beta-cell function, adjusted for age-related insulin resistance, decreases throughout life in first-degree relatives. This deterioration may be exacerbated in the presence of autoantibodies. Oral glucose tolerance test data combined with a nonlinear mixed-effect modeling population approach may be a useful technique to evaluate SI and secretion in a population.

Cranberry juice consumption is often recommended along with low-dose oral antibiotics for prophylaxis for recurrent urinary tract infection (UTI). Because multiple membrane transporters are involved in the intestinal absorption and renal excretion of beta-lactam antibiotics, we evaluated the potential risk of pharmacokinetic interactions between cranberry juice and the beta-lactams amoxicillin (amoxicilline) and cefaclor. The amoxicillin-cranberry juice interaction was investigated in 18 healthy women who received on four separate occasions a single oral test dose of amoxicillin at 500 mg and 2 g with or without cranberry juice cocktail (8 oz) according to a crossover design. A parallel cefaclor-cranberry juice interaction study was also conducted in which 500 mg cefaclor was administered with or without cranberry juice cocktail (12 oz). Data were analyzed by noncompartmental methods and nonlinear mixed-effects compartmental modeling. We conclude that the concurrent use of cranberry juice has no significant effect on the extent of oral absorption or the renal clearance of amoxicillin and cefaclor. However, delays in the absorption of amoxicillin and cefaclor were observed. These results suggest that the use of cranberry juice at usual quantities as prophylaxis for UTI is not likely to alter the pharmacokinetics of these two oral antibiotics.

Cyclophosphamide-based regimens are front-line treatment for numerous pediatric malignancies; however, current dosing methods result in considerable interpatient variability in tumor response and toxicity. In this pediatric population, the authors' objectives were (1) to quantify and explain the pharmacokinetic variability of cyclophosphamide and 2 of its metabolites, hydroxycyclophosphamide (HCY) and carboxyethylphosphoramide mustard (CEPM), and (2) to apply a population pharmacokinetic model to describe the disposition of cyclophosphamide and these metabolites. A total of 196 blood samples were obtained from 22 children with neuroblastoma receiving intravenous cyclophosphamide (400 mg/m2/d) and topotecan. Blood samples were quantitated for concentrations of cyclophosphamide, HCY, and CEPM using liquid chromatography-mass spectrometry and analyzed using nonlinear mixed-effects modeling with the NONMEM software system. After model building was complete, the area under the concentration-time curve (AUC) was computed using NONMEM. Cyclophosphamide elimination was described by noninducible and inducible routes, with the latter producing HCY. Glomerular filtration rate was a covariate for the fractional elimination of HCY and its conversion to CEPM. Considerable interpatient variability was observed in the AUC of cyclophosphamide, HCY, and CEPM. These results represent a critical first step in developing pharmacokinetic-linked pharmacodynamic studies in children receiving cyclophosphamide to determine the clinical relevance of the pharmacokinetic variability in cyclophosphamide and its metabolites.

Background: Intracranial hypertension due to primary and secondary injuries is a prime concern when providing care to patients with severe traumatic brain injury. Increases in intracranial pressure vary depending on compensatory processes within the craniospinal space, also referred to as intracranial adaptive capacity. In patients with traumatic brain injury and decreased intracranial adaptive capacity, intracranial pressure increases disproportionately in response to a variety of stimuli. However, no well-validated measures are available in clinical practice to predict the development of such an increase., Objectives: To examine whether P2 elevation, quantified by determining the P2:P1 ratio (=0.8) of the intracranial pressure pulse waveform, is a unique predictor of disproportionate increases in intracranial pressure on a beat-by-beat basis in the 30 minutes preceding the elevation in patients with severe traumatic brain injury, within 48 hours after deployment of an intracranial pressure monitor., Methods: A total of 38 patients with severe traumatic brain injury were sampled from a randomized controlled trial of cerebral perfusion pressure management in patients with traumatic brain injury or subarachnoid hemorrhage., Results: The P2 elevation was not only present before the disproportionate increase in pressure, but also appeared in the comparison data set (within-subject without such a pressure increase)., Conclusions: P2 elevation is not a reliable clinical indicator to predict an impending disproportionate increase in intracranial pressure.

Purpose: Probabilistic methods are insufficient for dealing with the vagueness inherent in human judgment of minimal data available during early drug development. We sought to use fuzzy set theory as a basis for quantifying and propagating vague judgment in a physiologically based pharmacokinetic (PBPK) model for diazepam disposition., Materials and Methods: First, using diazepam distribution data in rat tissues and fuzzy regression, we estimated fuzzy rat tissue-to-plasma partition coefficients (Kp's). We scaled the coefficients prior to human PBPK modeling. Next, we constructed the fuzzy set of hepatic intrinsic clearance (CLint) by integrating CLint values measured in vitro from human hepatocytes. Finally, we used these parameters, and other physiological and biochemical information, to predict human diazepam disposition. We compared the simulated plasma kinetics with published concentration-time profiles., Results: We successfully identified rat Kp's by fuzzy regression. The predicted rat tissue concentration-time contours enveloped the animal tissue distribution data. For the human PBPK model, the mean in vivo plasma concentrations were contained in the simulated concentration-time envelopes., Conclusions: We present a novel computational approach for handling information paucity in PBPK models using fuzzy arithmetic. Our methodology can model the vagueness associated with human perception and interpretation of minimal drug discovery data.

Osteoporosis is a metabolic bone disease resulting from increased bone resorption and characterized by low bone mass that leads to increased bone fragility and risk of fracture, particularly of the hip, spine and wrist. Bone resorption is dependent on receptor activator of NF-kappa B ligand (RANKL), which binds to RANK receptor on preosteoclasts to initiate osteoclastogenesis and maintains osteoclast function and survival. To neutralize the effects of RANKL, the body naturally produces the protein osteoprotegerin (OPG), which acts as a decoy receptor for RANKL and contributes to bone homeostasis. We describe the piecewise development of a three-compartment pharmacokinetic model with both linear and Michaelis-Menten eliminations, and an indirect pharmacodynamic response model to describe the pharmacokinetics and pharmacodynamics, respectively, of the fusion protein, Fc-osteoprotegerin (Fc-OPG), in healthy postmenopausal women. Subsequently, model verification was performed and used to address study design questions via simulation. The model was developed using data from eight cohorts (n = 13 subjects/cohort; Fc-OPG:placebo = 10:3) classified by dose level (0.1, 0.3, 1.0, or 3.0 mg/kg) and route of administration (intravenous [IV] or subcutaneous [SC]). Fc-OPG serum concentrations and urinary N-telopeptide/creatinine ratios (NTX) following both IV and SC administration were available. The model provided an adequate fit to the observed data and physiologically plausible parameter estimates. Model robustness was tested via a posterior predictive check with the model performing well in most cases. Subsequent clinical trial simulations demonstrated that a single 3.0-mg/kg SC dose of Fc-OPG would be expected to produce, at 14 days post-dose, a median NTX percentage change from baseline of -45% (with a 95% prediction interval ranging from -34% to -60%). Lastly, model ruggedness was evaluated using local and global sensitivity analysis methods. In conclusion, the model selection and simulation strategies we applied were rigorous, useful, and easily generalizable.

Population kinetic modeling approaches, implemented as nonlinear mixed effects models, are attracting growing interest in many fields of biomedicine thanks to their value in estimating population features from sparsely sampled data. However, their application often entails approximations of the original model function, whose effect is difficult to gauge in general. We apply negative log-likelihood profiling to assess the effect of model approximation on the glucose-insulin Minimal Model, and compare nonlinear mixed-effects approximate methods to two-stage methods. Our preliminary findings suggest that nonlinear mixed effects models provide accurate parameter estimates, but also point out that the reliability of such estimates may be affected by large population variability and small sample size.

Disposition of drugs in pregnant women is poorly understood in spite of widespread prescription of drugs to women during gestation. We have developed a whole body physiologically based pharmacokinetic (PBPK) model to explore the effects of pregnancy on pharmacokinetics. The model accounts for maternofetal changes over the course of gestation; physiological and drug-specific parameters are taken from literature. Here we preliminarily demonstrate the model's utility to predict midazolam pharmacokinetics following intravenous bolus dosing in women undergoing Caesarian section. Simulations of maternal venous plasma concentrations compare favorably with data extracted from historical studies.

Statistical techniques have been traditionally used to deal with parametric variation in pharmacokinetic and pharmacodynamic models, but these require substantial data for estimates of probability distributions. In the presence of limited, inaccurate or imprecise information, simulation with fuzzy numbers represents an alternative tool to handle parametric uncertainty. Existing methods for implementing fuzzy arithmetic may, however, have significant shortcomings in overestimating (e.g., conventional fuzzy arithmetic) and underestimating (e.g., vertex method) the output uncertainty. The purpose of the present study is to apply and compare the applicability of conventional fuzzy arithmetic, vertex method and two recently proposed numerical schemes, namely transformation and optimization methods, for uncertainty modeling in pharmacokinetic and pharmacodynamic fuzzy-parameterized systems. A series of test problems were examined, including empirical pharmacokinetic and pharmacodynamic models, a function non-monotonic in its parameters, and a whole body physiologically based pharmacokinetic model. Our results verified that conventional fuzzy arithmetic can lead to overestimation of response uncertainty and should be avoided. For the monotonic pharmacokinetic and pharmacodynamic models, the vertex method accurately predicted fuzzy-valued output while incurring the least computational cost. It turned out that the choice of a suitable method for fuzzy simulation of the non-monotonic function depended on the required accuracy of the results: the vertex method was capable of eliciting an initial approximate solution with few function evaluations; for more accurate results, the transformation method was the most superior approach in terms of accuracy per unit CPU time.

In recent years, development of rheumatoid arthritis (RA) drug therapy has been more directly targeted to counteract specific mechanisms of inflammation, and it is now believed that early aggressive treatment with disease modifying drugs is important to inhibit future structural joint damage. The development of these new treatments has increased the need for methodologies to assess disease activity in RA and monitor the effectiveness of drug therapy. Unlike X-ray, which shows only structural bone damage, magnetic resonance imaging (MRI) can depict soft tissue damage and synovitis, the primary pathology of RA. Recent studies have also indicated that MRI is sensitive to pathophysiologic changes that may predate radiographic erosions and may predict future joint damage. In this study, we have developed a computer automated analysis technique for MR wrist images that provides an objective measure of RA synovitis. This method applies a two-compartment pharmacokinetic model to every voxel of a dynamic contrast-enhanced MRI (DCE-MRI) dataset and outputs resulting parametric images. The aim of this technique is to not only objectively quantify the severity of rheumatoid synovitis, but to also locally determine where areas of serious disease activity are situated through kinetic modeling of blood-tissue exchange. Preliminary results show good correlation to early enhancement rate, which has previously been shown to be a useful clinical marker of RA activity. However, the use of tracer kinetic modeling methods potentially provides more specific information regarding underlying RA physiology. This approach could provide a useful new tool in RA patient management and could substantially improve RA therapeutic studies by calculating objective biomarkers of the disease state.

To date, the application of physiologically based pharmacokinetic (PBPK) models in support of drug discovery remains limited, in part due to information deficit and uncertainty regarding model parameters. Fuzzy set theory provides a suitable way to objectively account for parameter uncertainty in models. Here, we present a fuzzy set-based PBPK modeling framework and demonstrate its utility in predicting diazepam pharmacokinetics in human plasma, following intravenous dosing, from available animal in vivo and literature data. For computationally expensive PBPK models, the sparse grid method is proposed as an efficient alternative to commonly used fuzzy arithmetic algorithms for function simulation.