DigiLoCS: A leap forward in predictive organ-on-chip simulations

Abstract

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the conventional model and ii)explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Accurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

Fig 1. Digital Twin (DT) approach.

Contrasting conventional approach, the DT approach uses biological, hardware, and physicochemical information to map the biological processes on liver-chip more accurately to in silico, thereby maximising the information leveraged. This results in the disentanglement of active (metabolism) and passive (permeability, partitioning) processes. Created with biorender.com.

Fig 2. Chip 1 is for CnBio [13] and 3D spheroids [28] and chip 2 is for Javelin [20] architectures.

Q_mix is the mixing flow rate in mL/min. Created with biorender.com.

Fig 3. Translational workflow plan that integrates results from organ-on-chip with computer modelling to predict the kinetics of drugs in humans.

The digital twins of the humanised organ-on-chip systems, together with chip-specific information and physicochemical information, are developed in R. Created with biorender.com.

Fig 4. Digital twin-based model simulation of on-chip kinetics after fitting parameters for selected compounds.

Observed data are shown in blue dots, where the data for diclofenac, midazolam, and oxazepam are from Docci et al. [13], while propranolol is from Tsamandouras et al. [17]. The red, violet and green curve plots the drug concentration in the intracellular, medium and interstitium compartments, respectively: IC = intracellular, Ist = interstitium.

Fig 5. Local and global sensitivity of the parameters with respect to output intracellular concentration.

(a) Blue bars indicate that the output and the input changes in the same direction, and the red bar indicates that the output decreases when the input increases. (b) The blue and orange bars represent first-order and total-order indices, respectively.

Fig 6. Impact of DigiLoCs on predicting clinical clearance values compared to the conventional approach.

In total, a set of 32 compounds across three different in vitro liver systems have been investigated. The x-axis presents the ratio of predicted/observed clinical clearance values using either the DigiLoCs or the conventional approach, and the y-axis shows the frequency of the ratio observed.

Fig 7. Correlation between observed and predicted in vivo intrinsic clearance (CL_int) using the three-compartment liver chip for 12 drugs (Docci et al. 2022; Tsamandouras et al. 2017).

The solid line shows the line of unity, while the dotted line is 1.5-fold, and the dashed line has a 3-fold deviation.

Fig 8. Simulated kinetics of propranolol after a single oral dose (80 mg).

Pink dots are clinical observations (digitised from Borgström et al. [29]), while the blue solid line represents the mean of the patient population using the clinical observed clearance value. When using the clearance value (red line) based on conventional approaches, the area under the curve is 6-fold overpredicted. In contrast, using the digital twin-based clearance, the AUC is only 1.5-fold overpredicted, also simulating the right kinetics at 24 h (black curve). Shaded areas represent ± 1 SD.