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Background Pharmacokinetic data for bedaquiline in children are limited. We described the pharmacokinetics and safety of bedaquiline in South African children and adolescents receiving treatment for multidrug/rifampicin-resistant tuberculosis (MDR/RR-TB) in routine care. Methods In this observational cohort study, children aged 6–17 years receiving bedaquiline at recommended doses as part of MDR/RR-TB treatment underwent semi-intensive pharmacokinetic sampling. Bedaquiline and the M2 metabolite plasma concentrations were quantified, and nonlinear mixed-effects modeling performed. Pediatric data were described using a pre-established model of bedaquiline pharmacokinetics in adults. The exposure reference was 187 µg ⋅ h/mL, the median weekly area under the curve (AUC) of adults at week 24 of treatment with bedaquiline. Safety was assessed through monthly clinical, blood and electrocardiogram monitoring, and treatment outcomes described. Results Fifteen children (3 human immunodeficiency virus [HIV]-positive) with median age 13.3 years (range 6.5–16.3) were included. A bedaquiline pharmacokinetic model was adapted to be allometrically scaled in clearance and volume, centered in the median child population weight. Bedaquiline bioavailability was 57% of that in adults. Overall bedaquiline exposures were below target, and AUC reference attainment was achieved in only 3 (20%) children. Ten children experienced 27 adverse events at least possibly related to bedaquiline; no adverse events led to bedaquiline withdrawal. Two adverse events (arthritis and arthralgia) were considered severe, and 2 children had mild QT interval corrected for heart rate using Fridericia’s formula (QT) prolongation. Conclusions The evaluated doses of bedaquiline in children ≥ 6 years of age were safe but achieved slightly lower plasma concentrations compared to adults receiving the recommended dose, possibly due to delayed food intake relative to bedaquiline administration.

BackgroundSafer, better, and shorter treatments for multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) are an urgent global health need. The phase 3 clinical trial Nix-TB (NCT02333799) tested a 6-month treatment of MDR and XDR-TB consisting of high-dose linezolid, bedaquiline, and pretomanid (BPaL). In this study, we investigate the relationship between the pharmacokinetic characteristics of the drugs, patient characteristics and efficacy endpoints from Nix-TB.MethodsPharmacokinetic data were collected at weeks 2, 8, and 16. Efficacy endpoints including treatment outcomes, time to stable culture conversion, and longitudinal time to positivity in the mycobacterial growth indicator tube assay were each characterized using nonlinear mixed-effects modeling. Relationships between patient, treatment pharmacokinetics, and disease characteristics and efficacy endpoints were evaluated.ResultsData from 93 (85% of the total) participants were analyzed. Higher body mass index was associated with a lower incidence of unfavorable treatment outcomes. Median time to stable culture conversion was 3 months in patients with lower baseline burden compared with 4.5 months in patients with high baseline burden. Participants with minimal disease had steeper time to positivity trajectories compared with participants with high-risk phenotypes. No relationship between any drugs’ pharmacokinetics (drug concentration or exposure metrics) and any efficacy outcomes was observed.ConclusionsWe have successfully described efficacy endpoints of a BPaL regimen from the Nix-TB trial. Participants with high-risk phenotypes significantly delayed time to culture conversion and bacterial clearance. The lack of a relationship between pharmacokinetic exposures and pharmacodynamic biomarkers opens the possibility to use lower, safer doses, particularly for toxicity-prone linezolid.

Background Isoniazid (INH) metabolism depends on the N-acetyl transferase 2 (NAT2) enzyme, whose maturation process remains unknown in low birth weight (LBW) and preterm infants. We aimed to assess INH exposure and safety in infants receiving oral tuberculosis prevention. Methods This population pharmacokinetics (PK) analysis used INH and N-acetyl-isoniazid (ACL) concentrations in infants (BW ≤ 4 kg), including preterm, with follow-up for 6 months. PK parameters were described using nonlinear mixed effects modeling. Simulations were performed to assess INH exposure and optimal dosing regimens, using 2 targets: Cmax at 3–6 mg/L and area under the curve (AUC) ≥ 10.52 mg h/L. Results We included 57 infants (79% preterm, 84% LBW) in the PK analysis, with a median (range) gestational age of 34 (28.7–39.4) weeks. At the time of sampling, postnatal age was 2.3 (0.2–7.3) months and weight (WT) was 3.7 (0.9–9.3) kg. NAT2 genotype was available in 43 (75.4%) patients (10 slow, 26 intermediate, and 7 fast metabolizers). Ninety percent of NAT2 maturation was attained by 4.4 post-natal months. WT, postmenstrual age, and NAT2 genotype significantly influenced INH exposure, with a 5-fold difference in AUC between slow and fast metabolizers for the same dose. INH appeared safe across the broad range of exposure for 61 infants included in the safety analysis. Conclusions In LBW/preterm infants, INH dosing needs frequent adjustment to account for growth and maturation. Pharmacogenetics-based dosing regimens is the most powerful approach to deliver safe and equalized exposures for all infants, because NAT2 genotype highly impacts INH pharmacokinetic variability.

Background Moxifloxacin is a recommended drug for rifampin-resistant tuberculosis (RR-TB) treatment, but there is limited pediatric pharmacokinetic and safety data, especially in young children. We characterize moxifloxacin population pharmacokinetics and QT interval prolongation and evaluate optimal dosing in children with RR-TB. Methods Pharmacokinetic data were pooled from 2 observational studies in South African children with RR-TB routinely treated with oral moxifloxacin once daily. The population pharmacokinetics and Fridericia-corrected QT (QTcF)-interval prolongation were characterized in NONMEM. Pharmacokinetic simulations were performed to predict expected exposure and optimal weight-banded dosing. Results Eighty-five children contributed pharmacokinetic data (median [range] age of 4.6 [0.8–15] years); 16 (19%) were aged Conclusions Moxifloxacin doses above 10–15 mg/kg are likely required in young children to match adult exposures but require further safety assessment, especially when coadministered with other QT-prolonging agents.

Aims Busulfan and treosulfan are cytotoxic agents used in the conditioning regime prior to paediatric haematopoietic stem cell transplantation (HSCT). These agents cause suppression of myeloid cells leaving patients severely immunocompromised in the early post-HSCT period. The main objectives were: (i) to establish a mechanistic pharmacokinetic-pharmacodynamic (PKPD) model for the treatment and engraftment effects on neutrophil counts comparing busulfan and treosulfan-based conditioning, and (ii) to explore current dosing schedules with respect to time to HSCT. Methods Data on 126 patients, 72 receiving busulfan (7 months-18 years, 5.1-47.0 kg) and 54 treosulfan (4 months-17 years, 3.8-35.8 kg), were collected. In total, 8935 neutrophil count observations were recorded during the study period in addition to drug concentrations to develop a mechanistic PKPD model. Absolute neutrophil count profiles were modelled semimechanistically, accounting for transplant effects and differing set points pre- and post-transplant. Results PK were best described by 2-compartment models for both drugs. The Friberg semimechanistic neutropenia model was applied with a linear model for busulfan and a maximum efficacy model for treosulfan describing drug effects at various stages of neutrophil maturation. System parameters were consistent across both drugs. The HSCT was represented by an amount of progenitor cells enhancing the neutrophils' proliferation and maturation compartments. Alemtuzumab was found to enhance the proliferative rate under which the absolute neutrophil count begin to grow after HSCT. Conclusion A semimechanistic PKPD model linking exposure to either busulfan or treosulfan to the neutrophil reconstitution dynamics was successfully built. Alemtuzumab coadministration enhanced the neutrophil proliferative rate after HSCT. Treosulfan administration was suggested to be delayed with respect to time to HSCT, leaving less time between the end of the administration and stem cell infusion.

Imperfect medication adherence remains the biggest predictor of treatment failure for patients with tuberculosis. Missed doses during treatment lead to relapse, tuberculosis resistance, and further spread of disease. Understanding individual patient phenotypes, population pharmacokinetics, resistance development, drug distribution to tuberculosis lesions, and pharmacodynamics at the site of infection is necessary to fully measure the impact of adherence on patient outcomes. To decrease the impact of expected variabilityin drug intake on tuberculosis outcomes, an improvement in patient adherence and new forgiving regimens that protect against missed doses are needed. In this review, we summarize emerging technologies to improve medication adherence in clinical practice and provide suggestions on how digital adherence technologies can be incorporated in clinical trials and practice and the drug development pipeline that will lead to more forgiving regimens and benefit patients suffering from tuberculosis.

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

AIMS: Immunotherapy is a rising alternative to traditional treatment in breast cancer (BC) patients in order to transform cold into hot immune enriched tumours and improve responses and outcome. A computational modelling approach was applied to quantify modulation effects of immunotherapy and chemotherapy response on tumour shrinkage and progression‐free survival (PFS) in naïve BC patients. METHODS: Eighty‐three Her2‐negative BC patients were recruited for neoadjuvant chemotherapy with or without immunotherapy based on dendritic cell vaccination. Sequential tumour size measurements were modelled using nonlinear mixed effects modelling and linked to PFS. Data from another set of patients (n = 111) were used to validate the model. RESULTS: Tumour size profiles over time were linked to biomarker dynamics and PFS. The immunotherapy effect was related to tumour shrinkage (P < .05), with the shrinkage 17% (95% confidence interval: 2–23%) being higher in vaccinated patients, confirmed by the finding that pathological complete response rates in the breast were higher in the vaccinated compared to the control group (25.6% vs 13.6%; P = .04). The whole tumour shrinkage time profile was the major prognostic factor associated to PFS (P < .05), and therefore, immunotherapy influences indirectly on PFS, showing a trend in decreasing the probability of progression with increased vaccine effects. Tumour subtype was also associated with PFS (P < .05), showing that luminal A BC patients have better prognosis. CONCLUSIONS: Dendritic cell‐based immunotherapy is effective in decreasing tumour size. The semi‐mechanistic validated model presented allows the quantification of the immunotherapy treatment effects on tumour shrinkage and establishes the relationship between the dynamics of tumour size and PFS.

The identification of sensitive, specific, and reliable biomarkers that can be quantified in the early phases of tuberculosis treatment and predictive of long-term outcome is key for the development of an effective short-course treatment regimen. Time to positivity (TTP), a biomarker of treatment outcome against Mycobacterium tuberculosis, measures longitudinal bacterial growth in mycobacterial growth indicator tube broth culture and may be predictive of standard time to stable culture conversion (TSCC). In two randomized phase 2b trials investigating dose-ranging rifapentine (Studies 29 and 29X), 662 participants had sputum collected over 6 months where TTP, TSCC, and time to culture conversion were quantified. The goals of this post hoc study were to characterize longitudinal TTP profiles and to identify individual patient characteristics associated with delayed time to culture conversion. In order to do so, a nonlinear mixed-effects model describing longitudinal TTP was built. Independent variables associated with increased bacterial clearance (increased TTP), assessed by subject-specific and population-level trajectories, were higher rifapentine exposure, lower baseline grade of sputum acid-fast bacillus smear, absence of productive cough, and lower extent of lung infiltrates on radiographs. Importantly, sensitivity analysis revealed that major learning milestones in phase 2b trials, such as significant exposure-response and covariate relationships, could be detected using truncated TTP data as early as 6 weeks from start of treatment, suggesting alternative phase 2b study designs. The TTP model built depicts a novel phase 2b surrogate endpoint that can inform early assessment of experimental treatment efficacy and treatment failure or relapse in patients treated with shorter and novel TB treatment regimens, improving efficiency of phase 2 clinical trials. (The studies discussed in this paper have been registered at ClinicalTrials.gov under identifiers NCT00694629 and NCT01043575.).

BackgroundThe addition of Dendritic cell vaccines (DCV) to neoadjuvant chemotherapy (NAC) could induce immune biomarker changes in those patients with residual disease (RD) by transforming tumor microenvironment.MethodsCore-diagnostic biopsies and surgical specimens from 80 patients (38 in the Vaccinated Group plus NAC (VG) and 42 in the Control Group (CG) treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using Immunohistochemistry (IHC) and the Automated Cellular Imaging System (ACIS III) in the core-diagnostic biopsies and in the surgical specimen, to compare the amount of TILs in each group.ResultsA CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). TNBC patients in the CG showed a TILs drop from 2.71% in the biopsy to 0.18% in the surgical specimen (p = 0.5). We also found that a 66.7% (4/6) of TNBC patients from VG registered an increase in TILs after treatment as compared with 20% (1/5) of TNBC patients in the CG (p=0.24). This phenomenon is not observed in the other biologic subtypes.An association between before NAC CD8 TILs (4% cut-off point ) and pathological complete response in VG was found in univariate and multivariate analysis (OR=1.41, IC95% 1.05-1.90; p=0.02, and OR=2.0, IC95% 1.05-3.9; p=0.03, respectively).ConclusionOur findings suggest that patients with TNBC especially benefit from the stimulation of the antitumor immune system by using DCV pulsed with tumor antigens.Trial registration: NCT01431196. Registred 19 May 2016. EudraCT 2009- 017402-36.

BackgroundPerioperative chemotherapy (CT) or neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC) has been shown to improve survival compared to an exclusive surgical approach. However, most patients retain a poor prognosis due to important relapse rates. Population pharmacokinetic-pharmacodynamic (PK/PD) modeling may allow identifying at risk-patients. We aimed to develop a mechanistic PK/PD model to characterize the relationship between the type of neoadjuvant therapy, histopathologic response and survival times in locally advanced GC and GEJC patients.MethodsPatients with locally advanced GC and GEJC treated with neoadjuvant CT with or without preoperative CRT were analyzed. Clinical response was assessed by CT-scan and EUS. Pathologic response was defined as a reduction on pTNM stage compared to baseline cTNM. Metastasis development risk and overall survival (OS) were described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks.ResultsA low correlation was observed between clinical and pathologic TNM stage for both T (R = 0.32) and N (R = 0.19) categories. A low correlation between clinical and pathologic response was noticed (R = -0.29). The OS model adequately described the observed survival rates. Disease recurrence, cTNM stage ≥3 and linitis plastica absence, were correlated to a higher risk of death.ConclusionOur model adequately described clinical response profiles, though pathologic response could not be predicted. Although the risk of disease recurrence and survival were linked, the identification of alternative approaches aimed to tailor therapeutic strategies to the individual patient risk warrants further research.

Volasertib, a potent and selective polo-like kinase inhibitor, has shown to increase response rates and improve survival with a clinically manageable safety profile, administered alone and in combination with cytarabine in patients with acute myeloid leukaemia. The objectives of this analysis were to describe the pharmacokinetics of volasertib and cytarabine, administered as single agents or in combination. Three thousand, six hundred and six plasma volasertib concentrations from 501 patients receiving either volasertib alone, or in combination with cytarabine, and 826 plasma cytarabine concentrations from 650 patients receiving cytarabine as multiple subcutaneous injections per cycle either alone, or in combination with volasertib, were analysed using NONMEM Version 7.3. Covariates evaluated included demographic and disease-related parameters. The pharmacokinetics of volasertib were found to be dose independent from 150 to 550 mg. Body surface area and ethnicity showed significant effects in all the patients. This is reflected as an increase in drug exposure for Japanese patients, although this finding has to be interpreted with caution because only 7% of patients were part of that population group. Volasertib showed low-to-mild inter-individual variability in total clearance. For the case of cytarabine, its pharmacokinetics was affected by body surface area. Finally, volasertib and cytarabine did not influence the pharmacokinetic characteristics of each other. The pharmacokinetics of volasertib in patients with acute myeloid leukaemia alone or in combination with cytarabine is predictable and associated with low-to-mild patient variability with the exception of the high variability associated with the volume of distribution of the central compartment, having no effect on the area under the plasma concentration–time curve.

e12114 Background: Breast cancer (BC) is the most commonly diagnosed malignancy in women. Neoadjuvant chemotherapy selects patients with optimal pathological responses and increases tumorectomy versus total mastectomy. Changes in tumor size (CTS) are related to an early clinical benefit and to Progression Free Survival (PFS) and Overall Survival (OS) in BC patients. Thus, the identification of new prognostic factors in the neoadjuvant scenario is crucial. Objectives:To assess the link between tumor growth inhibition metrics (TGI) and progression free survival (PFS) based on data obtained from BC patients with neoadjuvant therapy Methods: Data were obtained from 218 patients with BC treated at the University Clinic of Navarra, diagnosed from January 2008 to February 2016, with a median age of 49 years (range 25 to 84). Classification of molecular subtypes was based on IHC and found as follows: Her2 (6%), LA ( 23%), LB ( 36%), LB-Her2 (10%) and TN ( 25%). Patients were treated with ddECx 4 followed Docetaxel x 4. Her2 patients had therapy with Trastuzumab. 18% of the patients (LB and TN) received vaccination with dendritic cells. Data were analysed under the population approach with NONMEN 7.3. A model accounting for the dynamics of tumor growth and antitumor effect of the neoadjuvant therapy was developed. The model describes tumor size as the sum of the longest diameters of target lesions as a function of time and drug exposure. A PFS model was developed to describe the PFS time distribution as a function of covariates Results: The TGI was able to individually and accurately describe the tumor shrinkage. Vaccinated patients had an increased shrinkage rate of 36% (p < 0.001) than those who were not (p < 0.001). PFS (months) was best described by a Weibull model, and greater tumor size at diagnosis (p < 0.05), smaller CTS (p < 0.05) and TN subtype (p < 0.001) were identified as poor prognostic factors Conclusions: A PFS model that uses a model-based estimate of tumor growth to predict the PFS of BC patients receiving neoadjuvant therapy has been developed. This model helps to gain early understanding of potential clinical benefit to facilitate go/no-go decision making.

1081Background: Levels of tumor infiltrating lymphocytes (TILs) at diagnosis have shown to improve survival in TNBC and HER2 overexpressing breast cancer (BC) patients. Indeed, high levels of TILs ...