Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates.

Investigating the impact of target lesion selection on drug effect evaluation and tumour growth rate determination using tumour growth inhibition models: Example of malignant pleural mesothelioma patients treated with cisplatin alone or in combination with pemetrexed.

In the last update of the RECIST criteria in 2009, it was proposed that the number of target lesions to be followed over time for response-to-treatment assessment be reduced from 10 to 5 lesions maximum, with up to 2 per organ. We explored the impact of reducing the number of target lesion on the assessment of drug effect in a randomised phase III clinical trial using a tumour growth inhibition (TGI) model. Tumour size measurements from 441 (out of 456) patients were used to build two datasets for which observations were the sum of longest diameters of all measurable lesions (ALL dataset) or following the RECIST 1.

Dose individualisation in oncology using chemotherapy-induced neutropenia: Example of docetaxel in non-small cell lung cancer patients.

Aims: Chemotherapy-induced neutropenia has been associated with an increase in overall survival in non-small cell lung cancer patients. Therefore, neutrophil counts could be an interesting biomarker for drug efficacy as well as linked directly to toxicity. For drugs where neutropenia is dose limiting, neutrophil counts might be used for monitoring drug effect and for dosing optimisation.

Impact of tumour size measurement inter-operator variability on model-based drug effect evaluation.

Purpose: During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model.

Methods: One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre.

Population Pharmacokinetic/ Pharmacodynamic Modelling of Auditory-Evoked Event-Related Potentials with Lorazepam.

Event-related potentials (ERPs) are commonly used in Neuroscience research, particularly the P3 waveform because it is associated with cognitive brain functions and is easily elicited by auditory or sensory inputs. ERPs are affected by drugs such as lorazepam, which increase the latency and decrease the amplitude of the P3 wave. In this study, auditory-evoked ERPs were generated in 13 older healthy volunteers using an oddball tone paradigm, after administration of single 0.

Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy.

Metronomic chemotherapy is usually associated with better tolerance than conventional chemotherapy, and encouraging response rates have been reported in various settings. However, clinical development of metronomic chemotherapy has been hampered by a number of limitations, including the vagueness of its definition and the resulting empiricism in protocol design. In this study, we developed a pharmacokinetic/pharmacodynamic mathematical model that identifies the most effective administration schedule for gemcitabine monotherapy.