Making In Vitro Tumor Models Whole Again. Wu KZ, Adine C, Mitriashkin A, Aw BJJ, Iyer NG, Fong ELS. Adv Healthc Mater. 2023;12(14):e2202279. 

This review emphasizes the need to improve patient-derived in vitro tumor models by better replicating the tumor microenvironment to enhance drug testing reliability. It underlines how stromal elements, including immune components and the architecture and biochemical feature of the supporting matrices, significantly modulate cell-to-cell interactions. The review highlights the need of integrating clinical data with in vitro outcomes to ameliorate drug discovery.

Engineering precision biomaterials for personalized medicine. B. A. Aguado, J. C. Grim, A. M. Rosales, J. J. Watson-Capps, K. S. Anseth.  Sci. Transl. Med. 2018; 10, eaam8645.

This work underlines the necessity of standardized operational units for the development of new biomaterials for biomedical applications. Specifically, the perspective focuses on materials for precision medicine, but the authors emphasize how data integration and defined procedures facilitate the design of new devices. They provide a robust example of how guidelines can be formulated, ameliorating the development of biomaterials for applications such as organ-on-chip systems.

Biomaterial-based platforms for tumour tissue engineering Curvello, R., Kast, V., Ordóñez-Morán, P. et al. Nat Rev Mater, 2023; 8, 314–330.

This review explores the development and application of 3D cancer models based on molecularly designed biomaterials to replicate the dimensional, biomechanical, and biochemical properties of tumor tissues. The work underscores the significance of correct biomaterials designed in advancing preclinical research, drug screening, personalized medicine, and immunotherapy assessment.

Development of digital patient twins can undoubtedly benefit from and fundamentally relies on the integration and fusion of heterogenous data provided by different sources, such as imaging, omics, electronic records, sensors, to better capture the biological complexity of underlying diseases. Various data integration approaches can be employed to build the digital twins, ranging from conventional methods to multiscale network models, ontologies-knowledge graphs, and deep multimodal data fusion—although the latter faces challenges due to data scarcity. Some of these applications are explored in the papers referenced here:

Deep Multimodal Data Fusion. Fei Zhao, Chengcui Zhang, and Baocheng Geng. 2024.  ACM Comput. Surv. 56, 9, Article 216

Multiscale networks in multiple sclerosis. Kennedy KE, Kerlero de Rosbo N, Uccelli A, Cellerino M, Ivaldi F, Contini P, et al. (2024) . PLoS Comput Biol 20(2): e1010980. 

Personalized Diabetes Management with Digital Twins: A Patient-Centric Knowledge Graph Approach.Sarani Rad, F.; Hendawi, R.; Yang, X.; Li, J.  J. Pers. Med. 2024, 14, 359. 

Inflammatory bowel disease biomarkers revealed by the human gut microbiome network M Hu, G Caldarelli, T Gili Scientific Reports 13 , 19428 (2023).

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients affected by non-small cell lung cancer P. Vernocchi, T. Gili, F. Conte, F Del Chierico, G. Conta, A. Miccheli, A Botticelli, P. Paci, G. Caldarelli, M. Nuti, P. Marchetti, L Putignani International J. of Molecular Sciences, 21, 8730 (2020).

Liver metabolism

Assessment of Variability in Drug Metabolism with Liver MPS. N. Tsamandouras, T. Kostrzewski, C. L. Stokes, L. G. Griffith, D. J. Hughes and M. Cirit. Journal of Pharmacology and Experimental Therapeutics January 1, 2017, 360 (1) 95-105.

NASH/NAFLD:

Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system. Kostrzewski T, Snow S, Battle AL, et al.  Commun Biol. 2021;4(1):1080. 

Kidney Tox with Translation:

Translational Assessment of Drug-Induced Proximal Tubule Injury Using a Kidney Microphysiological System. Maass C, Sorensen NB, Himmelfarb J, Kelly EJ, Stokes CL, Cirit M.  CPT Pharmacometrics Syst Pharmacol. 2019;8(5):316-325.

Design/Scaling of Chips:

Multi-functional scaling methodology for translational pharmacokinetic and pharmacodynamic applications using integrated MPS.Christian Maass, Cynthia L. Stokes, Linda G. Griffith, Murat Cirit, Integrative Biology, Volume 9, Issue 4,Pages 290–302.

Analysis of OoC data and optimisation of media change protocol:

Establishing quasi-steady state operations of microphysiological systems (MPS) using tissue-specific metabolic dependencies. Maass, C., Dallas, M., LaBarge, M.E. et al.  Sci Rep 8, 8015 (2018).

Digital twins for PK/PD:

DigiLoCS: A Leap Forward in Predictive Organ-on-Chip Simulations. Manoja Rajalakshmi Aravindakshan, Chittaranjan Mandal, Alex Pothen, Christian Maass

bioRxiv 2024.03.28.587123.

Radiopharmaceutical therapy on-a-chip: a perspective on microfluidic-driven digital twins towards personalized cancer therapies. H Abdollahi, B Saboury, M Soltani, K Shi, C Uribe, A Rahmim, Science Bulletin, Volume 68, Issue 18, 2023, Pages 1983-1988,

Digital twins are integral to personalizing medicine and improving public health. Johnson, B., Curtius, K.  Nat Rev Gastroenterol Hepatol 21, 740–741 (2024). 

Patient-derived organoids in precision cancer medicine. Tong L, Cui W, Zhang B, Fonseca P, Zhao Q, Zhang P, Xu B, Zhang Q, Li Z, Seashore-Ludlow B, Yang Y, Si L, Lundqvist A. Med. 2024 Nov 8;5(11):1351-1377. doi: 10.1016/j.medj.2024.08.010. Epub 2024 Sep 27. PMID: 39341206.

This review highlights the potential of PDOs for personalized treatment strategies, biomarker discovery, drug screening, and modeling complex tumor-immune interactions, especially through advanced methodologies like organoids-on-chip technology and co-culture systems with immune and stromal cells.

A PIK3CA-mutant breast cancer metastatic patient-derived organoid approach to evaluate alpelisib treatment for multiple secondary lesions. Donzelli S, Cioce M, Sacconi A, Zanconato F, Daralioti T, Goeman F, Orlandi G, Di Martino S, Fazio VM, Alessandrini G, Telera S, Carosi M, Ciliberto G, Botti C, Strano S, Piccolo S, Blandino G. Mol Cancer. 2022 Jul 22;21(1):152. doi: 10.1186/s12943-022-01617-6. PMID: 35869553; PMCID: PMC9306102.

This manuscript provides a proof-of-concept for using PDOs from metastatic lesions to assess treatment responses, specifically the effectiveness of alpelisib, a PI3K-α inhibitor. The study showcases the potential for PDOs to accurately reflect patient-specific genetic and molecular profiles, offering insights into individualized treatment strategies.

A Review of Cell-Based Computational Modeling in Cancer Biology John Metzcar et al.,. JCO Clin Cancer Inform 3, 1-13(2019).

"This review highlight multiple techniques to create computational models of cancer cell. The approaches presented vary according to the granularity level and implemented features, each with its own advantages and disadvantages. The methods proposed in the review are illustrated through examples of cancer hypoxia, angiogenesis, invasion and immunosurveillence"

 Building digital twins of the human immune system: toward a roadmap. Laubenbacher, R., Niarakis, A., Helikar, T. et al.. npj Digit. Med. 5, 64 (2022). 

"This perspective proposes a framework for creating detailed, dynamic digital replicas of the immune system to advance personalized healthcare. This kind of apporach has the potential to improve disease prevention, treatment strategies, and drug development by tailoring interventions to individual immune responses."

 Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. Johnson JAI, et al. bioRxiv [Preprint].

"This preprint showcase a new methodology to create multicellular computational model and setup in-silico experiment using natural language statements."

Immune digital twins for complex human pathologies: applications, limitations, and challenges Niarakis, A., Laubenbacher, R., An, G. et al. npj Syst Biol Appl 10, 141 (2024). 

 A cellular Potts model for the MMP-dependent and -independent cancer cell migration in matrix microtracks of different dimensions. Scianna, M., Preziosi, L. Comput Mech 53, 485–497 (2014). 

A Cellular Potts Model for Analyzing Cell Migration across Constraining Pillar Arrays. Scianna, M.; Preziosi, L.. Axioms 2021, 10, 32. 

 Modeling the influence of nucleus elasticity on cell invasion in fiber networks and microchannels. Scianna M, Preziosi L.. J Theor Biol. 2013 Jan 21;317:394-406.

A Cellular Potts Model simulating cell migration on and in matrix environments. Scianna M, Preziosi L, Wolf K. Math Biosci Eng. 2013 Feb;10(1):235-61.

The NCI-MATCH trial: lessons for precision oncology O’Dwyer, P.J., Gray, R.J., Flaherty, K.T. et al. Nat Med 29, 1349–1357 (2023)  . 

"On the limitation on genomic driven license" 

Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations  Anthony Letai, Patrick Bhola, Alana L. Welm Science Direct (2023)

"The new world of Functional precision medicine: a review of Anthony Lethai, describing its relevance in the current environment"

 Phenotypic drug discovery: recent successes, lessons learned and new directions Vincent, F., Nueda, A., Lee, J. et al. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat Rev Drug Discov 21, 899–914 (2022). 

Bridging live-cell imaging and next-generation cancer treatment Alieva, M., Wezenaar, A.K.L., Wehrens, E.J. et al. Bridging live-cell imaging and next-generation cancer treatment. Nat Rev Cancer 23, 731–745 (2023). 

"On the role of phenotypic drug discovery, and assessing the impact of drugs on morphology"