Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. 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. 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. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. 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. 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. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.
Bioreactors have been used both to develop new, and to improve bioprocess yields for, biopharmaceutical products. However, efforts to miniaturize bioreactors, in order to save costs and accelerate process development times, have been limited by the lack of on-site monitoring capabilities available at such scales. In this study, small volume (3 nL) nonconsumptive holographic sensors were integrated into a glass-PDMS microfluidic chip to monitor via a blue-shift in the resultant holographic replay wavelength, the change in pH during microbial growth of Lactobacillus casei (L. casei) Shirota. Within the optimal growth pH range of L. casei, the accuracy of the miniaturized pH sensors was comparable to that of a conventional pH meter. Conceivably, this approach could be extrapolated to an array of miniaturized holographic sensors sensitive to different analytes, and thereby paving the way for reliable, real-time, noninvasive monitoring of microorganisms in a nanobioreactor.
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