Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection

In a recent multicenter population pharmacokinetic study of ciprofloxacin administered to children suffering from complicated urinary tract infection (cUTI), the apparent volume of distribution (V) and total plasma clearance (CL) were decreased by 83.6% and 41.5% respectively, compared to healthy children. To understand these differences, a physiologically-based pharmacokinetic model (PBPK) for ciprofloxacin was developed for cUTI children. First, a PBPK model in adults was developed, modified incorporating age-dependent functions and evaluated with paediatric data generated from a published model in healthy children. Then, the model was then adapted to a cUTI paediatric population according to the degree of renal impairment (KF) affecting renal clearance (CL_Renal,) and CYP1A2 clearance (CL_CYP1A2). Serum and urine samples obtained from 22 cUTI children were used for model evaluation. Lastly, a parameter sensitivity analysis identified the most influential parameters on V and CL. The PBPK model predicted the ciprofloxacin exposure in adults and children, capturing age-related pharmacokinetic changes. Plasma concentrations and fraction excreted unchanged in urine (f_e) predictions improved in paediatric cUTI patients once CL_renal and CL_CYP1A2 were corrected by KF. The presented PBPK model for ciprofloxacin demonstrates its adequacy to simulate different dosing scenarios to obtain PK predictions in a healthy population from 3 months old onwards. Model adaptation of CL_Renal and CL_CYP1A2 according to KF explained partially the differences seen in the plasma drug concentrations and f_e vs time profiles between healthy and cUTI children. Nevertheless, it is necessary to further investigate the disease-related changes in cUTI to improve model predictions.

Optimal dynamic control approach in a multi-objective therapeutic scenario: Application to drug delivery in the treatment of prostate cancer

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.

The Long Neglected Player: Modeling Tumor Uptake to Guide Optimal Dosing

Pharmacokinetic modeling, traditionally using drug exposure, is widely used to support decision-making in translational medicine and patient care. The development of mechanistic computational models that integrate drug concentrations at the site of action making use of existing knowledge opens a new paradigm in optimal dosing. Clin Cancer Res; 24(14); 3236-8. ©2018 AACRSee related article by Ribba et al., p. 3325.

Computer simulations for bioequivalence trials: Selection of analyte in BCS class II and IV drugs with first-pass metabolism, two metabolic pathways and intestinal efflux transporter

A semi-physiological two compartment pharmacokinetic model with two active metabolites (primary (PM) and secondary metabolites (SM)) with saturable and non-saturable pre-systemic efflux transporter, intestinal and hepatic metabolism has been developed. The aim of this work is to explore in several scenarios which analyte (parent drug or any of the metabolites) is the most sensitive to changes in drug product performance (i.e. differences in in vivo dissolution) and to make recommendations based on the simulations outcome. A total of 128 scenarios (2 Biopharmaceutics Classification System (BCS) drug types, 2 levels of K_{M Pgp}, in 4 metabolic scenarios at 2 dose levels in 4 quality levels of the drug product) were simulated for BCS class II and IV drugs. Monte Carlo simulations of all bioequivalence studies were performed in NONMEM 7.3. Results showed the parent drug (PD) was the most sensitive analyte for bioequivalence trials in all the studied scenarios. PM and SM revealed less or the same sensitivity to detect differences in pharmaceutical quality as the PD. Another relevant result is that mean point estimate of C_max and AUC methodology from Monte Carlo simulations allows to select more accurately the most sensitive analyte compared to the criterion on the percentage of failed or successful BE studies, even for metabolites which frequently show greater variability than PD.

Correlation between anti-PD-L1 tumor concentrations and tumor-specific and nonspecific biomarkers in a melanoma mouse model

Blockade of PD-L1 with specific monoclonal antibodies (anti-PD-L1) represents a therapeutic strategy to increase the capability of the immune system to modulate the tumor immune-resistance. The relationship between anti-PD-L1 tumor exposition and anti-tumor effect represents a challenge that has been addressed in this work through the identification of certain biomarkers implicated in the antibody's mechanism of action, using a syngeneic melanoma mouse model. The development of an in-vitro/in-vivo platform has allowed us to investigate the PD-L1 behavior after its blockage with anti-PD-L1 at cellular level and in animals. In-vitro studies showed that the complex PD-L1/anti-PD-L1 was retained mainly at the cell surface. The antibody concentration and time exposure affected directly the recycling or ligand turnover. In-vivo studies showed that anti-PD-L1 was therapeutically active at all stage of the disease, with a rapid onset, a low but durable efficacy and non-relevant toxic effect. This efficacy measured as tumor shrinkage correlated with tumor-specific infiltrating lymphocytes (TILs), which increased as antibody tumor concentrations increased. Both, TILS and antibody concentrations followed similar kinetic patterns, justifying the observed anti-PD-L1 rapid onset. Interestingly, peripheral lymphocytes (PBLs) behave as infiltrating lymphocytes, suggesting that these PBLs might be considered as a possible biomarker for antibody activity.

Advanced Boolean modeling of biological networks applied to systems pharmacology

Motivation

Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.

Results

In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.

Availability and Implementation

The source code is available at https://github.com/SPIDDOR/SPIDDOR .

Contact

itzirurzun@alumni.unav.es , itroconiz@unav.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Commentary on Pharmacometrics for Immunotherapy

This commentary provides an overview of recent examples of pharmacometrics applied during the clinical development of two antagonists of the programmed death‐1 (PD‐1) cell surface receptor, pembrolizumab and nivolumab. Despite the remarkable achievements obtained in predicting the correct dosing schedule from different quantitative approaches, data indicated a great degree of heterogeneity in tumor response. To achieve therapeutic goals the search for predictive biomarkers associated with a lack of response and mechanism‐based combination studies are warranted.

Population pharmacokinetic model of lithium and drug compliance assessment

Population pharmacokinetic analysis of lithium during therapeutic drug monitoring and drug compliance assessment was performed in 54 patients and 246 plasma concentrations levels were included in this study. Patients received several treatment cycles (1-9) and one plasma concentration measurement for each patient was obtained always before starting next cycle (pre-dose) at steady state. Data were analysed using the population approach with NONMEM version 7.2. Lithium measurements were described using a two-compartment model (CL/F=0.41Lh^-1, V₁/F=15.3L, Q/F=0.61Lh^-1, and V₂/F = 15.8L) and the most significant covariate on lithium CL was found to be creatinine clearance (reference model). Lithium compliance was analysed using inter-occasion variability or Markovian features (previous lithium measurement as ordered categorical covariate) on bioavailability parameter. Markov-type model predicted the lithium compliance in the next cycle with higher success rate (79.8%) compared to IOV model (65.2%) and reference model (43.2%). This model becomes an efficient tool, not only being able to adequately describe the observed outcome, but also to predict the individual drug compliance in the next cycle. Therefore, Bipolar disorder patients can be classified regarding their probability to become extensive or poor compliers in the next cycle and then, individual probabilities lower than 0.5 highlight the need of intensive monitoring, as well as other pharmaceutical care measurements that might be applied to enhance drug compliance for a better and safer lithium treatment.

EGF-liposomes promote efficient EGFR targeting in xenograft colocarcinoma model

Aim: Development of EGF-liposomes (LP-EGF) for selective molecules delivery in tumors expressing EGFR. Material & methods: In vitro cellular interaction of EGF-LP and nontargeted liposomes (LP-N) was assayed at 37 and 4°C in cells expressing different EGFR levels. Receptor-mediated uptake was investigated by competition with a monoclonal antibody anti-EGFR. Selective intracellular drug delivery and efficacy was tested by oxaliplatin encapsulation. In vivo biodistribution of LP-N and LP-EGF was done in xenograft model. Results: LP-EGF was internalized by an active and selective mechanism through EGFR without receptor activation. Oxaliplatin LP-EGF decreased IC50 between 48 and 13% in cell EGFR+. LP-EGF was accumulated in tumor over 72 h postdosing, while LP-N in spleen. Conclusion: LP-EGF represents an attractive nanosystem for cancer therapy or diagnosis.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.

Pharmacokinetic-pharmacodynamic modelling in anaesthesia

Anaesthesiologists adjust drug dosing, administration system and kind of drug to the characteristics of the patient. They then observe the expected response and adjust dosing to the specific requirements according to the difference between observed response, expected response and the context of the surgery and the patient. The approach above can be achieved because on one hand quantification technology has made significant advances allowing the anaesthesiologist to measure almost any effect by using noninvasive, continuous measuring systems. On the other the knowledge on the relations between dosing, concentration, biophase dynamics and effect as well as detection of variability sources has been achieved as being the benchmark specialty for pharmacokinetic-pharmacodynamic (PKPD) modelling. The aim of the review is to revisit the most common PKPD models applied in the field of anaesthesia (i.e. effect compartmental, turnover, drug-receptor binding and drug interaction models) through representative examples. The effect compartmental model has been widely used in this field and there are multiple applications and examples. The use of turnover models has been limited mainly to describe respiratory effects. Similarly, cases in which the dissociation process of the drug-receptor complex is slow compared with other processes relevant to the time course of the anaesthetic effect are not frequent in anaesthesia, where in addition to a rapid onset, a fast offset of the response is required. With respect to the characterization of PD drug interactions different response surface models are discussed. Relevant applications that have changed the way modern anaesthesia is practiced are also provided.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.

IMPLICATIONS FOR PRACTICE

The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

Tramadol and o-desmethyl tramadol clearance maturation and disposition in humans: a pooled pharmacokinetic study

BACKGROUND AND OBJECTIVES

We aimed to study the impact of size, maturation and cytochrome P450 2D6 (CYP2D6) genotype activity score as predictors of intravenous tramadol disposition.

METHODS

Tramadol and O-desmethyl tramadol (M1) observations in 295 human subjects (postmenstrual age 25 weeks to 84.8 years, weight 0.5-186 kg) were pooled. A population pharmacokinetic analysis was performed using a two-compartment model for tramadol and two additional M1 compartments. Covariate analysis included weight, age, sex, disease characteristics (healthy subject or patient) and CYP2D6 genotype activity. A sigmoid maturation model was used to describe age-related changes in tramadol clearance (CLPO), M1 formation clearance (CLPM) and M1 elimination clearance (CLMO). A phenotype-based mixture model was used to identify CLPM polymorphism.

RESULTS

Differences in clearances were largely accounted for by maturation and size. The time to reach 50 % of adult clearance (TM50) values was used to describe maturation. CLPM (TM50 39.8 weeks) and CLPO (TM50 39.1 weeks) displayed fast maturation, while CLMO matured slower, similar to glomerular filtration rate (TM50 47 weeks). The phenotype-based mixture model identified a slow and a faster metabolizer group. Slow metabolizers comprised 9.8 % of subjects with 19.4 % of faster metabolizer CLPM. Low CYP2D6 genotype activity was associated with lower (25 %) than faster metabolizer CLPM, but only 32 % of those with low genotype activity were in the slow metabolizer group.

CONCLUSIONS

Maturation and size are key predictors of variability. A two-group polymorphism was identified based on phenotypic M1 formation clearance. Maturation of tramadol elimination occurs early (50 % of adult value at term gestation).

Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation

AIMS

The aims of this study were (i) to develop a population pharmacokinetic (PK) model of tacrolimus in a Mexican renal transplant paediatric population (n = 53) and (ii) to test the influence of different covariates on its PK properties to facilitate dose individualization.

METHODS

Population PK and variability parameters were estimated from whole blood drug concentration profiles obtained at steady-state using the non-linear mixed effect modelling software NONMEM® Version 7.2.

RESULTS

Tacrolimus PK profiles exhibited high inter-patient variability (IPV). A two compartment model with first order input and elimination described the tacrolimus PK profiles in the studied population. The relationship between CYP3A5 genotype and tacrolimus CL/F was included in the final model. CL/F in CYP3A5*1/*1 and *1/*3 carriers was approximately 2- and 1.5-fold higher than in CYP3A5*3/*3 carriers (non-expressers), respectively, and explained almost the entire IPV in CL/F. Other covariates retained in the final model were the tacrolimus dose and formulation type. Limustin® showed markedly lower concentrations than the rest of the formulations.

CONCLUSIONS

Population PK modelling of tacrolimus in paediatric renal transplant recipients identified the tacrolimus formulation type as a significant covariate affecting the blood concentrations and confirmed the previously reported significant effect of CYP3A5 genotype on CL/F. It allowed the design of a proposed dosage based on the final model that is expected to help to improve tacrolimus dosing.

Validation of a semi-physiological model for caffeine in healthy subjects and cirrhotic patients

The objective of this paper was to validate a previously developed semi physiological model to simulate bioequivalence trials of drug products. The aim of the model was to ascertain whether the measurement of the metabolite concentration-time profiles would provide any additional information in bioequivalence studies (Fernandez-Teruel et al., 2009a,b; Navarro-Fontestad et al., 2010). The semi-physiological model implemented in NONMEM VI was used to simulate caffeine and its main metabolite plasma levels using caffeine parameters from bibliography. Data from 3 bioequivalence studies in healthy subjects at 3 different doses (100, 175 and 400mg of caffeine) and one study in cirrhotic patients (200 or 250mg) were used. The first aim was to adapt the previous semi-physiological model for caffeine, showing the hepatic metabolism with one main metabolite, paraxanthine. The second aim was to validate the model by comparison of the simulated plasma levels of parent drug and metabolite to the experimental data. The simulations have shown that the proposed semi-physiological model was able to reproduce adequately the pharmacokinetic behavior of caffeine and paraxanthine in both healthy subjects and cirrhotic patients at all the assayed doses. Therefore, the model could be used to simulate plasma concentrations vs. time of drugs with the same pharmacokinetic scheme as caffeine, as long as their population parameters are known, and it could be useful for bioequivalence trial simulation of drugs that undergo hepatic metabolism with a single main metabolite.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules

The current work integrates cell-cycle dynamics occurring in the bone marrow compartment as a key element in the structure of a semimechanistic pharmacokinetic/pharmacodynamic model for neutropenic effects, aiming to describe, with the same set of system- and drug-related parameters, longitudinal data of neutropenia gathered after the administration of the anticancer drug diflomotecan (9,10-difluoro-homocamptothecin) under different dosing schedules to patients (n = 111) with advanced solid tumors. To achieve such an objective, the general framework of the neutropenia models was expanded, including one additional physiologic process resembling cell cycle dynamics. The main assumptions of the proposed model are as follows: within the stem cell compartment, proliferative and quiescent cells coexist, and only cells in the proliferative condition are sensitive to drug effects and capable of following the maturation chain. Cell cycle dynamics were characterized by two new parameters, FProl (the fraction of proliferative [Prol] cells that enters into the maturation chain) and kcycle (first-order rate constant governing cell cycle dynamics within the stem cell compartment). Both model parameters were identifiable as indicated by the results from a bootstrap analysis, and their estimates were supported by date from the literature. The estimates of FProl and kcycle were 0.58 and 1.94 day(-1), respectively. The new model could properly describe the neutropenic effects of diflomotecan after very different dosing scenarios, and can be used to explore the potential impact of dosing schedule dependencies on neutropenia prediction.

Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology

Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.