Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.
The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real‐life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.
In this study, we tested the possibility of calculating permeability of porous scaffolds utilized in soft tissue engineering using pore size and shape. We validated the results using experimental measured pressure drop and simulations with the inclusion of structural deformation. We prepared Polycaprolactone (PCL) and Chitosan-Gelatin (CG) scaffolds by salt leaching and freeze drying technique, respectively. Micrographs were assessed for pore characteristics and mechanical properties. Porosity for both scaffolds was nearly same but the permeability varied 10-fold. Elastic moduli were 600 and 9 kPa for PCL and CG scaffolds, respectively, while Poisson's ratio was 0.3 for PCL scaffolds and ∼1.0 for CG scaffolds. A flow-through bioreactor accommodating a 10 cm diameter and 0.2 cm thick scaffold was used to determine the pressure-drop at various flow rates. Additionally, computational fluid dynamic (CFD) simulations were performed by coupling fluid flow, described by Brinkman equation, with structural mechanics using a dynamic mesh. The experimentally obtained pressure drop matched the simulation results of PCL scaffolds. Simulations were extended to a broad range of permeabilities (10(-10) m(2) to 10(-14) m(2) ), elastic moduli (10-100,000 kPa) and Poisson's ratio (0.1-0.49). The results showed significant deviation in pressure drop due to scaffold deformation compared to rigid scaffold at permeabilities near healthy tissues. Also, considering the scaffold as a nonrigid structure altered the shear stress profile. In summary, scaffold permeability can be calculated using scaffold pore characteristics and deformation could be predicted using CFD simulation. These relationships could potentially be used in monitoring tissue regeneration noninvasively via pressure drop.
Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.
In this study, the distribution of oxygen and glucose was evaluated along with consumption by hepatocytes using three different approaches. The methods include (i) Computational Fluid Dynamics (CFD) simulation, (ii) residence time distribution (RTD) analysis using a step-input coupled with segregation model or dispersion model, and (iii) experimentally determined consumption by HepG2 cells in an open-loop. Chitosan-gelatin (CG) scaffolds prepared by freeze-drying and polycaprolactone (PCL) scaffolds prepared by salt leaching technique were utilized for RTD analyses. The scaffold characteristics were used in CFD simulations i.e. Brinkman's equation for flow through porous medium, structural mechanics for fluid induced scaffold deformation, and advection-diffusion equation coupled with Michaelis-Menten rate equations for nutrient consumption. With the assumption that each hepatocyte behaves like a micro-batch reactor within the scaffold, segregation model was combined with RTD to determine exit concentration. A flow rate of 1 mL/min was used in the bioreactor seeded with 0.6 × 10(6) HepG2 cells/cm(3) on CG scaffolds and oxygen consumption was measured using two flow-through electrodes located at the inlet and outlet. Glucose in the spent growth medium was also analyzed. RTD results showed distribution of nutrients to depend on the surface characteristics of scaffolds. Comparisons of outlet oxygen concentrations between the simulation results, and experimental results showed good agreement with the dispersion model. Outlet oxygen concentrations from segregation model predictions were lower. Doubling the cell density showed a need for increasing the flow rate in CFD simulations. This integrated approach provide a useful strategy in designing bioreactors and monitoring tissue regeneration.
In this study, transport characteristics in flow-through and parallel-flow bioreactors used in tissue engineering were simulated using computational fluid dynamics. To study nutrient distribution and consumption by smooth muscle cells colonizing the 100 mm diameter and 2-mm thick scaffold, effective diffusivity of glucose was experimentally determined using a two-chambered setup. Three different concentrations of chitosan-gelatin scaffolds were prepared by freezing at -80°C followed by lyophilization. Experiments were performed in both bioreactors to measure pressure drop at different flow rates. At low flow rates, experimental results were in agreement with the simulation results for both bioreactors. However, increase in flow rate beyond 5 mL/min in flow-through bioreactor showed channeling at the circumference resulting in lower pressure drop relative to simulation results. The Peclet number inside the scaffold indicated nutrient distribution within the flow-through bioreactor to be convection-dependent, whereas the parallel-flow bioreactor was diffusion-dependent. Three alternative design modifications to the parallel-flow were made by (i) introducing an additional inlet and an outlet, (ii) changing channel position, and (iii) changing the hold-up volume. Simulation studies were performed to assess the effect of scaffold thickness, cell densities, and permeability. These new designs improved nutrient distribution for 2 mm scaffolds; however, parallel-flow configuration was found to be unsuitable for scaffolds more than 4-mm thick, especially at low porosities as tissues regenerate. Furthermore, operable flow rate in flow-through bioreactors is constrained by the mechanical strength of the scaffold. In summary, this study showed limitations and differences between flow-through and parallel-flow bioreactors used in tissue engineering.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.