Translational in vitro research: integrating 3D drug discovery and development processes into the drug development pipeline

2019 ◽  
Vol 24 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Jens M. Kelm ◽  
Madhu Lal-Nag ◽  
Gurusingham Sitta Sittampalam ◽  
Marc Ferrer
ADMET & DMPK ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 201-211 ◽  
Author(s):  
Pankajini Mallick

In vitro-in vivo extrapolation (IVIVE) integrated in physiologically-based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development processes to predict human pharmacokinetic (PK) parameters. Drug transporters can influence drug pharmacokinetics and are key aspects contributing to the development of a successful drug. This review provides a snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics. The paper also describes the potential of IVIVE-PBPK models as prospective approaches to predict the role of drug transporters in drug discovery and development.


2021 ◽  
pp. 247255522110309
Author(s):  
Olive Jung ◽  
Min Jae Song ◽  
Marc Ferrer

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.


2018 ◽  
Vol 39 (1) ◽  
pp. 4-15 ◽  
Author(s):  
Adedamola Olayanju ◽  
Lauren Jones ◽  
Karin Greco ◽  
Christopher E. Goldring ◽  
Tahera Ansari

Bioanalysis ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 199-201
Author(s):  
Fan Jin ◽  
Daniel Tang ◽  
Kelly Dong ◽  
Dafang Zhong

This article provides an update on new development of China Bioanalysis Forum (CBF). CBF became a member association of Chinese Pharmaceutical Association (CPA) at the end of 2019. The official ceremony and first scientific symposium were held in Shanghai on 18 September 2020. The president of Chinese Pharmaceutical Association and representatives from industry, Contract Research Organization (CRO), hospitals and academic institutes attended the ceremony. Seven experts in the field gave presentations on various topics including Drug Metabolism and Pharmacokinetics (DMPK) and bioanalytical support in drug discovery and development as well as experience in Traditional Chinese Medicine research. With the continuous growth of research and development in China, it is well acknowledged that bioanalysis provides critical support for new innovative medicines and generic drug development in the region.


2020 ◽  
Vol 64 (7) ◽  
Author(s):  
E. D. Pieterman ◽  
M. J. Sarink ◽  
C. Sala ◽  
S. T. Cole ◽  
J. E. M. de Steenwinkel ◽  
...  

ABSTRACT One of the reasons for the lengthy tuberculosis (TB) treatment is the difficulty to treat the nonmultiplying mycobacterial subpopulation. In order to assess the ability of (new) TB drugs to target this subpopulation, we need to incorporate dormancy models in our preclinical drug development pipeline. In most available dormancy models, it takes a long time to create a dormant state, and it is difficult to identify and quantify this nonmultiplying condition. The Mycobacterium tuberculosis 18b strain might overcome some of these problems, because it is dependent on streptomycin for growth and becomes nonmultiplying after 10 days of streptomycin starvation but still can be cultured on streptomycin-supplemented culture plates. We developed our 18b dormancy time-kill kinetics model to assess the difference in the activity of isoniazid, rifampin, moxifloxacin, and bedaquiline against log-phase growth compared to the nonmultiplying M. tuberculosis subpopulation by CFU counting, including a novel area under the curve (AUC)-based approach as well as time-to-positivity (TTP) measurements. We observed that isoniazid and moxifloxacin were relatively more potent against replicating bacteria, while rifampin and high-dose bedaquiline were equally effective against both subpopulations. Moreover, the TTP data suggest that including a liquid culture-based method could be of additional value, as it identifies a specific mycobacterial subpopulation that is nonculturable on solid media. In conclusion, the results of our study underline that the time-kill kinetics 18b dormancy model in its current form is a useful tool to assess TB drug potency and thus has its place in the TB drug development pipeline.


2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.


2019 ◽  
Vol 4 (7) ◽  
Author(s):  
Samuel Egieyeh ◽  
Sarel F. Malan ◽  
Alan Christoffels

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.


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