Using data collected from nine phase I/II/III clinical studies (n = 527, subcutaneous or intravenous administration), a TMDD model considering the quasi-steady-state approximation was developed to characterize the interaction dynamics of alirocumab and PCSK9, combined with an indirect pharmacodynamic model describing the inhibition of LDL-C by PCSK9 in a one-step approach using nonlinear-mixed effects modeling. The aim of this work was to develop an integrated pharmacokinetic–pharmacodynamic model to describe the interaction of alirocumab with PCSK9 and its impact on the evolution of low-density lipoprotein cholesterol (LDL-C) levels and explore labeling specification for subpopulations.Methods The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.Īlirocumab is a cholesterol-lowering monoclonal antibody targeting proprotein convertase subtilisin kexin type 9 (PCSK9) indicated in the prevention of cardiovascular risk and exhibiting target-mediated drug disposition (TMDD).
#METHOD MAP NONMEM SOFTWARE#
This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.Ĭancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. Furthermore, building a PM model is more time- and labor-intensive compared with ML. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. For example, for AUC0–24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h
#METHOD MAP NONMEM SERIES#
XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC0–24h) after repeated dosing. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. In contrast, ML models are much quicker trained, but offer less mechanistic insights. Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD).
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