[Translation into French and republication of: "Anticoagulant treatment of cancer-associated thromboembolism"]

Venous thromboembolism (VTE) is a frequent and potentially fatal complication in patients with cancer. During the initial period after the thromboembolic event, a patient receiving anticoagulant treatment is exposed both to a risk of VTE recurrence and also to an elevated bleeding risk conferred by the treatment. For this reason, the choice of anticoagulant is critical. The choice should take into account patient-related factors (such as functional status, age, body mass index, platelet count and renal function), VTE-related factors (such as severity or site), cancer-related factors (such as activity and progression) and treatment related factors (such as drug-drug interactions), which all potentially influence bleeding risk, and patient preference. These should be evaluated carefully for each patient during a multidisciplinary team meeting. For most patients, apixaban or a low molecular-weight heparin is the most appropriate initial choice for anticoagulant treatment. Such treatment should be offered to all patients with active cancer for at least 6months. The patient and treatment should be re-evaluated regularly, and anticoagulant treatment changed when necessary. Continued anticoagulant treatment beyond 6months is justified if the cancer remains active or if the patient experienced recurrence of VTE in the first 6months. In other cases, the interest of continued anticoagulant treatment may be considered on an individual patient basis in collaboration with oncologists.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

Population pharmacokinetics of atacicept in systemic lupus erythematosus: An analysis of three clinical trials

B cell stimulating factor (BLyS) and a proliferation-inducing ligand (APRIL) are targets for novel treatments in patients with systemic lupus erythematosus (SLE). Atacicept is a recombinant, soluble fusion protein that blocks BLyS and APRIL activity. This study characterized the pharmacokinetic (PK) profile of atacicept using a population PK model and identified covariates explaining the PK variability. Total atacicept concentrations from a phase I study in healthy volunteers and two phase II studies in patients with SLE, using subcutaneous administration, were modeled using a quasi-steady-state approximation of the target-mediated drug disposition model with first-order absorption. The model included 3640 serum atacicept concentration records from 37 healthy volunteers and 503 patients with SLE and described total atacicept concentrations of the three trials, providing precise estimates of all parameters. Body weight and baseline BLyS concentration were the only statistically significant covariates, whereas no differences were found between patients and healthy volunteers. Apparent clearance and volume of the central compartment increased with body weight and initial target concentration increased with baseline BLyS. The change on atacicept exposure was moderate, with a difference in area under the curve compared with the median of 20%-32% for body weight, and 7%-18% for BLyS. Therefore, the effects of these covariates on atacicept exposure are not expected to be clinically relevant. The model described the complete total atacicept concentration-time profiles without finding any differences between healthy subjects and patients with SLE and supports the 150 mg once weekly dose for further trials.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging

PURPOSE

A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology.

METHODS

We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports.

RESULTS

The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89).

CONCLUSION

We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Determination of a clinically effective evobrutinib dose: Exposure-response analyses of a phase II relapsing multiple sclerosis study

The pharmacometric analysis of the double-blind, randomized, phase II study (NCT02975349) investigating the safety and efficacy of evobrutinib, explored exposure-response relationships and suitable dosing regimens of evobrutinib for relapsing multiple sclerosis. Population pharmacokinetic (PK)/pharmacodynamic modeling was applied to data collected in fasted patients treated with placebo or evobrutinib (25 mg once-daily [q.d.], 75 mg q.d., or 75 mg twice-daily [b.i.d.]) for 24 weeks, followed by a 24-week blinded extension (placebo patients switched to 25 mg q.d.). Model-based exposures for PK and Bruton's tyrosine kinase occupancy (BTKO) were used for exposure-response analyses (maximum 207 patients). PK, BTKO profiles, and annualized relapse rate (ARR) after 48 weeks of treatment under alternative dosing regimens were simulated. Exposure-response modeling identified a relationship between evobrutinib exposure and clinical response for total number of T1 Gd+ and new/enlarging T2 lesions at weeks 12-24, and ARR at week 48. Area under the concentration-time curve over 24 h at steady-state (AUC_0-24,SS ) of 468 and ≥400 ng/ml h was associated with T1 Gd+/T2 lesion reduction and ARR improvement, respectively. These exposures were associated with steady-state (SS) predose BTKO ≥95%. Based on PK and BTKO profile simulations, evobrutinib 75 mg b.i.d. while fasted is predicted to maintain SS predose BTKO >95% in 92% of patients. Evobrutinib 45 mg b.i.d. with food is predicted to achieve similar exposure as 75 mg b.i.d. while fasted (predose BTKO >95% in 93% of patients). Evobrutinib 45 mg b.i.d. with food is predicted to have comparable exposure and BTKO to 75 mg b.i.d. without food (phase II) and will be pharmacologically effective and appropriate for clinical use in phase III multiple sclerosis studies.

Population pharmacokinetic and pharmacodynamic modeling of evobrutinib in healthy adult participants

Evobrutinib, a Bruton's tyrosine kinase (BTK) inhibitor, has shown therapeutic potential in relapsing multiple sclerosis. This analysis aimed to develop pharmacokinetic (PK) and pharmacodynamic (PD; BTK occupancy [BTKO]) models of evobrutinib and simulate PK and BTKO profiles under alternative dosing regimens. Data were obtained from two phase I evobrutinib studies in healthy adult participants (Japanese and non-Japanese). Overall, 2326 observations were available from 76 participants; n = 42 from Study MS200527_0017 Part A received evobrutinib 25, 75, or 200 mg once-daily oral doses for 6 days while fasted; n = 18 from Study MS200527_0019 and n = 16 from Study MS200527_0017 Part B received single evobrutinib 75 mg oral doses with food (low-fat meal) and while fasted. Population PK/PD modeling for evobrutinib concentrations and BTKO (fraction unbound) were performed using nonlinear mixed-effects modeling. The effect of once-daily/twice-daily regimens and doses of 10-200 mg on BTKO were simulated. A two-compartment model with sequential zero-first order absorption and first-order elimination adequately described the data. Bioavailability increased by 49% with food compared with when fasted. There was no difference in PK parameters between Japanese and non-Japanese participants. The BTKO profile of evobrutinib was described by the irreversible binding population model. The simulated percentage of participants with minimum BTKO increased in a dose-dependent manner across the BTKO thresholds of interest (70%, 80%, 90%, and 95% occupancy). Evobrutinib doses of 25 mg once-daily, 50 mg twice-daily, or 75 mg twice-daily while fasted are possible choices for further development, assuming BTKO ≥70% at trough is needed to achieve efficacy.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models

One of the objectives of oncology phase I dose-escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model-based designs are becoming mainstream in oncology first-in-human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose-toxicity models to estimate MTD. We extend traditional dose-toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure-toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose-escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule-based and model-based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model-based designs were better than rule-based methods and that exposure-toxicity model-based methods have the potential to valuably complement dose-toxicity model-based methods. Exposure-toxicity model-based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose-escalation studies.

Multisensory integration in neurons of the medial pulvinar of macaque monkey

The pulvinar is a heterogeneous thalamic nucleus, which is well developed in primates. One of its subdivisions, the medial pulvinar, is connected to many cortical areas, including the visual, auditory, and somatosensory cortices, as well as with multisensory areas and premotor areas. However, except for the visual modality, little is known about its sensory functions. A hypothesis is that, as a region of convergence of information from different sensory modalities, the medial pulvinar plays a role in multisensory integration. To test this hypothesis, 2 macaque monkeys were trained to a fixation task and the responses of single-units to visual, auditory, and auditory-visual stimuli were examined. Analysis revealed auditory, visual, and multisensory neurons in the medial pulvinar. It also revealed multisensory integration in this structure, mainly suppressive (the audiovisual response is less than the strongest unisensory response) and subadditive (the audiovisual response is less than the sum of the auditory and the visual responses). These findings suggest that the medial pulvinar is involved in multisensory integration.

Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials

Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing–remitting MS (from CLARITY and CLARITY‐Extension trials) and patients experiencing a first demyelinating event (from the ORACLE‐MS trial). We applied gradient‐boosting (from XgBoost library) and Shapley Additive Explanations (SHAP) methods to identify patients' covariates that predict disease activity 3 and 6 months before their clinical observation, including patient baseline characteristics, longitudinal magnetic resonance imaging readouts, and neurological and laboratory measures. The most predictive covariates for early identification of disease activity in patients were found to be treatment duration, higher number of new combined unique active lesion count, higher number of new T1 hypointense black holes, and higher age‐related MS severity score. The outcome of this analysis improves our understanding of the mechanism of onset of disease activity in patients with MS by allowing their early identification in clinical settings and prompting preventive measures, therapeutic interventions, or more frequent patient monitoring.

Exposure-response analyses for the MET inhibitor tepotinib including patients in the pivotal VISION trial: support for dosage recommendations

Purpose

Tepotinib is a highly selective MET inhibitor approved for treatment of non-small cell lung cancer (NSCLC) harboring METex14 skipping alterations. Analyses presented herein evaluated the relationship between tepotinib exposure, and efficacy and safety outcomes.

Methods

Exposure–efficacy analyses included data from an ongoing phase 2 study (VISION) investigating 500 mg/day tepotinib in NSCLC harboring METex14 skipping alterations. Efficacy endpoints included objective response, duration of response, and progression-free survival. Exposure–safety analyses included data from VISION, plus four completed studies in advanced solid tumors/hepatocellular carcinoma (30–1400 mg). Safety endpoints included edema, serum albumin, creatinine, amylase, lipase, alanine aminotransferase, aspartate aminotransferase, and QT interval corrected using Fridericia’s method (QTcF).

Results

Tepotinib exhibited flat exposure–efficacy relationships for all endpoints within the exposure range observed with 500 mg/day. Tepotinib also exhibited flat exposure–safety relationships for all endpoints within the exposure range observed with 30–1400 mg doses. Edema is the most frequently reported adverse event and the most frequent cause of tepotinib dose reductions and interruptions; however, the effect plateaued at low exposures. Concentration-QTc analyses using data from 30 to 1400 mg tepotinib resulted in the upper bounds of the 90% confidence interval being less than 10 ms for the mean exposures at the therapeutic (500 mg) and supratherapeutic (1000 mg) doses.

Conclusions

These analyses provide important quantitative pharmacologic support for benefit/risk assessment of the 500 mg/day dosage of tepotinib as being appropriate for the treatment of NSCLC harboring METex14 skipping alterations.

Registration Numbers

NCT01014936, NCT01832506, NCT01988493, NCT02115373, NCT02864992.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04441-3.

Population pharmacokinetic analysis of tepotinib, an oral MET kinase inhibitor, including data from the VISION study

Purpose

Tepotinib is a highly selective, potent, mesenchymal–epithelial transition factor (MET) inhibitor, approved for the treatment of non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping. Objectives of this population pharmacokinetic (PK) analysis were to evaluate the dose–exposure relationship of tepotinib and its major circulating metabolite, MSC2571109A, and to identify the intrinsic/extrinsic factors that are predictive of PK variability.

Methods

Data were included from 12 studies in patients with cancer and in healthy participants. A sequential modeling approach was used to analyze the parent and metabolite data, including covariate analyses. Potential associations between observed covariates and PK parameters were illustrated using bootstrap analysis-based forest plots.

Results

A two-compartment model with sequential zero- and first-order absorption, and a first-order elimination from the central compartment, best described the plasma PK of tepotinib in humans across the dose range of 30–1400 mg. The bioavailability of tepotinib was shown to be dose dependent, although bioavailability decreased primarily at doses above the therapeutic dose of 500 mg. The intrinsic factors of race, age, sex, body weight, mild/moderate hepatic impairment and mild/moderate renal impairment, along with the extrinsic factors of opioid analgesic and gefitinib intake, had no relevant effect on tepotinib PK. Tepotinib has a long effective half-life of ~ 32 h.

Conclusions

Tepotinib shows dose proportionality up to at least the therapeutic dose, and time-independent clearance with a profile appropriate for once-daily dosing. None of the covariates identified had a clinically meaningful effect on tepotinib exposure or required dose adjustments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04423-5.

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

PURPOSE

Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti-programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)-tumor growth dynamics (TGD) models.

PATIENTS AND METHODS

Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models.

RESULTS

In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition.

CONCLUSIONS

We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination.

Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab

Avelumab (anti–PD‐L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable‐importance assessments) was used to build models to identify prognostic/predictive factors associated with long‐term OS and tumor growth dynamics (TGDs). Baseline, re‐baseline, and longitudinal variables were evaluated as covariates in a parametric time‐to‐event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log‐logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma‐glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil‐lymphocyte ratio, lactate dehydrogenase, or C‐reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time‐varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re‐baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

Optimization of dose selection using multiple surrogates of toxicity as a continuous variable in phase I cancer trial

In phase I trials, it is the top priority of clinicians to effectively treat patients and minimize the chance of exposing them to subtherapeutic and overly toxic doses, while exploiting patient information. Motived by this practical consideration, we revive the one parameter linear dose-finder developed in 1970s to accommodate a continuous toxicity response in the phase I cancer clinical trials, which is called the two parameters linear dose-finder (2PLD). The 2PLD is a fully Bayesian model that assumes a linear relationship between toxicity response and dose. We suggest a dose search algorithm based on the 2PLD to exploit the grades of toxicities from multiple adverse events to align with Common Toxicity Criteria for Adverse Events provided by the National Cancer Institute. The proposed search procedure suggests an optimal dose to each patient by using accrued patients' information while controlling the posterior probability of overdose. The heterogeneity of patients in dose reaction is addressed by making a fully Bayesian inference about the standard deviation of toxicity responses. The 2PLD can be an attractive tool for clinical scientists due to its parsimonious description of a toxicity-dose curve and medical interpretation as well as an automatic posterior computation. We illustrate the performance of this design using simulation data to identify the maximum tolerated dose.

Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer

AIMS

The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS).

METHODS

A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored.

RESULTS

Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS.

CONCLUSIONS

Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.

[Plastic surgery and fighting: Our experience during Nagorno-Karabakh war in 2020]

INTRODUCTION

This work relates the experience of three French surgical missions in the care of the war wounded during the armed conflict in Nagorno Karabakh which took place from September 27 to November 10, 2020.

MATERIALS AND METHODS

Three surgical missions were carried out in Armenia between October 2020 and January 2021. Surgeons intervened in different hospitals, at different times of the conflict and on various war wounds.

RESULTS

The presence of a plastic surgeon proved to be essential in the care of war wounded, especially in delayed emergency and secondary care. The ortho-plastic treatment offered during these missions has proven to be effective in the reconstruction of limbs. These missions made it possible to introduce the induced membrane technique of Masquelet AC in Armenia. During our visit to the Yerevan burn center, we mentioned the very probable use of white phosphorus as an etiology in several of the cases analyzed.

CONCLUSION

We relate the particular experience of civilian surgeons in the context of a modern armed conflict. The presence of a plastic surgeon proved to be indispensable in the care of war wounded and especially in their secondary reconstructions.

Translational pharmacokinetic-pharmacodynamic modeling of preclinical and clinical data of the oral MET inhibitor tepotinib to determine the recommended phase II dose

Tepotinib is a highly selective and potent MET inhibitor in development for the treatment of patients with solid tumors. Given the favorable tolerability and safety profiles up to the maximum tested dose in the first‐in‐human (FIH) trial, an efficacy‐driven translational modeling approach was proposed to establish the recommended phase II dose (RP2D). To study the in vivo pharmacokinetics (PKs)/target inhibition/tumor growth inhibition relationship, a subcutaneous KP‐4 pancreatic cell‐line xenograft model in mice with sensitivity to MET pathway inhibition was selected as a surrogate tumor model. Further clinical PK and target inhibition data (derived from predose and postdose paired tumor biopsies) from a FIH study were integrated with the longitudinal PKs and target inhibition profiles from the mouse xenograft study to establish a translational PK/pharmacodynamic (PD) model. Preclinical data showed that tumor regression with tepotinib treatment in KP‐4 xenograft tumors corresponded to 95% target inhibition. We therefore concluded that a PD criterion of sustained, near‐to‐complete (>95%) phospho‐MET inhibition in tumors should be targeted for tepotinib to be effective. Simulations of dose‐dependent target inhibition profiles in human tumors that exceeded the PD threshold in more than 90% of patients established an RP2D of tepotinib 500 mg once daily. This translational mathematical modeling approach supports an efficacy‐driven rationale for tepotinib phase II dose selection of 500 mg once daily. Tepotinib at this dose has obtained regulatory approval for the treatment of patients with non‐small cell lung cancer harboring MET exon 14 skipping.

Location and temporal memory of objects declines in aged marmosets (Callithrix jacchus)

Episodic memory decline is an early marker of cognitive aging in human. Although controversial in animals and called “episodic-like memory”, several models have been successfully developed, however they rarely focused on ageing. While marmoset is an emerging primate model in aging science, episodic-like memory has never been tested in this species and importantly in aged marmosets. Here, we examined if the recall of the what-when and what-where building blocks of episodic-like memory declines in ageing marmosets. We developed a naturalistic approach using spontaneous exploration of real objects by young and old marmosets in the home cage. We implemented a three-trial task with 1 week inter-trial interval. Two different sets of identical objects were presented in sample trials 1 and 2, respectively. For the test trial, two objects from each set were presented in a former position and two in a new one. We quantified the exploratory behaviour and calculated discrimination indices in a cohort of 20 marmosets. Young animals presented a preserved memory for combined what-where, and what-when components of the experiment, which declined with aging. These findings lead one to expect episodic-like memory deficits in aged marmosets.

Hydro-climatic changes of wetlandscapes across the world

Assessments of ecosystem service and function losses of wetlandscapes (i.e., wetlands and their hydrological catchments) suffer from knowledge gaps regarding impacts of ongoing hydro-climatic change. This study investigates hydro-climatic changes during 1976-2015 in 25 wetlandscapes distributed across the world's tropical, arid, temperate and cold climate zones. Results show that the wetlandscapes were subject to precipitation (P) and temperature (T) changes consistent with mean changes over the world's land area. However, arid and cold wetlandscapes experienced higher T increases than their respective climate zone. Also, average P decreased in arid and cold wetlandscapes, contrarily to P of arid and cold climate zones, suggesting that these wetlandscapes are located in regions of elevated climate pressures. For most wetlandscapes with available runoff (R) data, the decreases were larger in R than in P, which was attributed to aggravation of climate change impacts by enhanced evapotranspiration losses, e.g. caused by land-use changes.

Comparison of Various Phase I Combination Therapy Designs in Oncology for Evaluation of Early Tumor Shrinkage Using Simulations

There is still a lack of efficient designs for identifying the dose response in oncology combination therapies in early clinical trials. The concentration response relationship can be identified using the early tumor shrinkage time course, which has been shown to be a good early response marker of clinical efficacy. The performance of various designs using an exposure–tumor growth inhibition model was explored using simulations. Different combination effects of new drug M and cetuximab (reference therapy) were explored first assuming no effect of M on cetuximab (to investigate the type I error (α)), and subsequently assuming additivity or synergy between cetuximab and M. One‐arm, two‐arm, and four‐arm designs were evaluated. In the one‐arm design, 60 patients received cetuximab + M. In the two‐arm design, 30 patients received cetuximab and 30 received cetuximab + M. In the four‐arm design, in addition to cetuximab and cetuximab + M as standard doses, combination arms with lower doses of cetuximab were evaluated (15 patients/arm). Model‐based predictions or “simulated observations” of early tumor shrinkage at week 8 (ETS8) were compared between the different arms. With the same number of individuals, the one‐arm design showed better statistical power than other designs but led to strong inflation of α in case of misestimated reference for ETS8 value. The two‐arm design protected against this misestimation and, with the same total number of subjects, would provide higher statistical power than a four‐arm design. However, a four‐arm design would be helpful for exploring more doses of cetuximab in combination with M to better understand the interaction.

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple‐dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed‐effects modeling approach. A two‐compartment model with parallel Michaelis–Menten and time‐decreasing linear clearance adequately described the data, the latter being related to post‐treatment response. With respect to bias in exposure metrics, the simplified time‐varying linear clearance (CL) model was the best alternative. Time‐variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady‐state was crucial for unbiased estimation of Michaelis–Menten elimination in case of the reference (parallel Michaelis–Menten and time‐varying linear CL) model.

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient's CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08-1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions' TS dynamics could improve oncology models in favor of a better prediction of OS.

Effectiveness of Therapeutic Patient Education Interventions for Older Adults with Cancer: A Systematic Review

The incidence of cancer increases with age and demographics shows that the population of western countries is dramatically ageing. The new discipline of Geriatric Oncology is emerging aiming at providing tailored and patient-centred support to older adults with cancer. With the development of oral cancer therapy and outpatient treatments, Therapeutic Patient Education (TPE), aiming at enabling the patient and their relatives to cope with the disease in partnership with health professionals, appears to be an interesting and useful tool. The purpose of this paper is to search for evidence of the effectiveness of educational interventions for patients in older adults with cancer. The first screening found 2,617 articles, of which 150 were eligible for review. Among them, fourteen finally met the inclusion criteria: experimental and quasi-experimental studies enrolling older adults (over 65 years old), suffering from cancer and receiving an educational intervention. The types of educational intervention were diverse in these studies (support by phone and web base material). The results appear to be positive on anxiety, depression and psychological distress, patient knowledge and pain. However, data currently available on the effectiveness of a TPE program in Geriatric Oncology is lacking. Further studies are needed to assess the effectiveness of TPE programs adapted to the specific circumstances of the older adult.

First-in-Man Phase I Trial of the Selective MET Inhibitor Tepotinib in Patients with Advanced Solid Tumors

PURPOSE

Tepotinib is an oral, potent, highly selective MET inhibitor. This first-in-man phase I trial investigated the MTD of tepotinib to determine the recommended phase II dose (RP2D).

PATIENTS AND METHODS

Patients received tepotinib orally according to one of three dose escalation regimens (R) on a 21-day cycle: R1, 30-400 mg once daily for 14 days; R2, 30-315 mg once daily 3 times/week; or R3, 300-1,400 mg once daily. After two cycles, treatment could continue in patients with stable disease until disease progression or unacceptable toxicity. The primary endpoint was incidence of dose-limiting toxicity (DLT) and treatment-emergent adverse events (TEAE). Secondary endpoints included safety, tolerability, pharmacokinetics, pharmacodynamics, and antitumor effects.

RESULTS

One hundred and forty-nine patients received tepotinib (R1: n = 42; R2: n = 45; R3: n = 62). Although six patients reported DLTs [one patient in R1 (115 mg), three patients in R2 (60, 100, 130 mg), two patients in R3 (1,000, 1,400 mg)], the MTD was not reached at the highest tested dose of 1,400 mg daily. The RP2D of tepotinib was established as 500 mg once daily, supported by translational modeling data as sufficient to achieve ≥95% MET inhibition in ≥90% of patients. Treatment-related TEAEs were mostly grade 1 or 2 fatigue, peripheral edema, decreased appetite, nausea, vomiting, and lipase increase. The best overall response in R3 was partial response in two patients, both with MET overexpression.

CONCLUSIONS

Tepotinib was well tolerated with clinical activity in MET-dysregulated tumors. The RP2D of tepotinib was established as 500 mg once daily. MET abnormalities can drive tumorigenesis. This first-in-man trial demonstrated that the potent, highly selective MET inhibitor tepotinib can reduce or stabilize tumor burden and is well tolerated at doses up to 1,400 mg once daily. An RP2D of 500 mg once daily, as determined from translational modeling and simulation integrating human population pharmacokinetic and pharmacodynamic data in tumor biopsies, is being used in ongoing clinical trials.

Changing Body Weight-Based Dosing to a Flat Dose for Avelumab in Metastatic Merkel Cell and Advanced Urothelial Carcinoma

Avelumab, an anti–programmed death‐ligand 1 monoclonal antibody approved for the treatment of metastatic Merkel cell carcinoma and platinum‐treated urothelial carcinoma, was initially approved with a 10 mg/kg weight‐based dose. We report pharmacokinetic (PK)/pharmacodynamic analyses for avelumab comparing weight‐based dosing and a flat 800 mg dose, developed using data from 1,827 patients enrolled in 3 clinical trials (NCT01772004, NCT01943461, and NCT02155647). PK metrics were simulated for weight‐based and flat‐dosing regimens and summarized by quartiles of weight. Derived exposure metrics were used in simulations of exposure‐safety (various tumors) and exposure‐efficacy (objective responses; Merkel cell or urothelial carcinoma). Flat dosing was predicted to provide similar exposure to weight‐based dosing, with slightly lower variability. Exposure‐safety and exposure‐efficacy simulations suggested similar benefit:risk profiles for the two dosing regimens. These pharmacometric analyses provided the basis for the US Food and Drug Administration approval of a flat dose of avelumab 800 mg every 2 weeks in approved indications.

Extravasation of calcium solution in the child: Value of negative-pressure wound therapy

The extravasation of a calcium solution into soft tissue constitutes a medical emergency, and a lack of adequate management can lead to significant functional and cosmetic sequelae. Here, we report on the management of and long-term outcome in two children who experienced calcium infusion leakage. We also describe the emergency procedures used in cases of extravasation and discuss the role of negative pressure wound therapy as an appropriate adjunct to conventional techniques for dealing with serious extravasation-related injuries.

[The role of electromagnetic navigation bronchoscopy in the diagnosis of peripheral pulmonary lesions]

INTRODUCTION

Electromagnetic navigation bronchoscopy (ENB) is a recent, minimally invasive procedure utilized to guide endoscopic diagnostic tools to peripheral pulmonary nodules. The place of this technology among other diagnostic procedures remains uncertain.

METHOD

We analyzed our 30 first months of ENB used in the diagnosis of 106 lesions in 101 patients, from June 2016 to December 2018. Follow-up and final diagnosis was completed for 95 lesions (90%).

RESULTS

ENB was performed for 3.5% of all patients referred for abnormal findings on pulmonary imaging, and represented 19% of second line procedures for peripheral pulmonary lesions. Procedures were performed under general anesthesia, with a mean duration of 35min. The sensitivity of ENB was 64% (95%CI: 52-74%) for lesions with a mean diameter of 21mm, with an improvement over time (sensitivity 69% in the last 18 months). The presence of a bronchus within the lesion (bronchus sign) was associated with an increased sensitivity of 74%. Pneumothorax occurred in 5 patients (5%) of which 4 required drainage. There was no hemoptysis, and no death related to the procedure.

CONCLUSION

ENB is a minimally invasive procedure reaching acceptable sensitivity in the most difficult patients. ENB can be recommended for the diagnosis of peripheral pulmonary nodules when no other procedure is successful or possible. Its use as a first choice procedure is, for the moment, limited by the cost, but must be weighed against that of non-diagnostic procedures, and the cost of complications of trans-thoracic lung biopsies.

Time-Varying Clearance and Impact of Disease State on the Pharmacokinetics of Avelumab in Merkel Cell Carcinoma and Urothelial Carcinoma

Avelumab, a human anti-programmed death ligand 1 immunoglobulin G1 antibody, has shown efficacy and manageable safety in multiple tumors. A two-compartment population pharmacokinetic model for avelumab incorporating intrinsic and extrinsic covariates and time-varying clearance (CL) was identified based on data from 1,827 patients across three clinical studies. Of 14 tumor types, a decrease in CL over time was more notable in metastatic Merkel cell carcinoma and squamous cell carcinoma of the head and neck, which had maximum decreases of 32.1% and 24.7%, respectively. The magnitude of reduction in CL was higher in responders than in nonresponders. Significant covariate effects of baseline weight, baseline albumin, and sex were identified on both CL and central distribution volume. Significant covariate effects of black/African American race, C-reactive protein, and immunogenicity were found on CL. None of the covariate or time-dependent effects were clinically important or warranted dose adjustment.

Boutiques: a flexible framework to integrate command-line applications in computing platforms

We present Boutiques, a system to automatically publish, integrate, and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitates the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework.