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.

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.

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.

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.

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-ß 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.

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.

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.

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 (AUC0-24,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.

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.

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.

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.

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.

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.

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.

Population pharmacokinetic model selection assisted by machine learning.

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

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.

Fast screening of covariates in population models empowered by machine learning.

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

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.