Errors, Contaminations, and Confounding Factors in in vivo Studies: Challenges and Practical Solutions
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Abstract
In vivo studies remain a cornerstone of biomedical, pharmacological, and toxicological studies, providing critical insights into the safety and efficacy of novel interventions. However, the reliability and translational significance of these experiments are often compromised by methodological mistakes, hidden contaminations, and uncontrolled confounding factors. By identifying threats to validity and offering practical solutions, the current review aimed to enhance reproducibility, reduce unnecessary expenditure of time and resources, and improve the ethical and scientific integrity of in vivo studies. Poor study design, insufficient randomization, and operator-related inconsistencies introduce variability that may obscure true biological effects. Similarly, viral or microbial infections, environmental contaminants in feed or bedding, and cross-contamination between animals can profoundly alter immune, metabolic, or behavioral outcomes, often without being detected until results prove inconsistent. Furthermore, factors such as temperature, light cycles, handling stress, circadian rhythms, and biological characteristics of the animals introduce additional layers of complexity, leading to irreproducible or contradictory findings. The present study consolidated the existing evidence on the primary sources of errors, contamination, and confounding factors in in vivo studies, supported by practical case examples. Additionally, the present study emphasized best practices for mitigation, such as standardizing protocols, following animal research guidelines in in vivo experiments, utilizing specific-pathogen-free animals, continuously monitoring environmental and health parameters, and providing comprehensive staff training. Thus, emerging solutions such as automation, artificial intelligence, and the increasing incorporation of in vitro and in silico alternatives were explored as methods to decrease reliance on animal testing models.
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References
Gray M, Guido S, and Kugadas A. The use of large animal models to improve pre-clinical translational research. Front Vet Sci. 2022; 9: 1086912. DOI: 10.3389/fvets.2022.1086912
Vashishat A, Patel P, Das Gupta G, and Das Kurmi B. Alternatives of animal models for biomedical research: A comprehensive review of modern approaches. Stem Cell Rev Rep. 2024; 20(4): 881-899. DOI: 10.1007/s12015-024-10701-x
Pepin X, Arora S, Borges L, Cano-Vega M, Carducci T, Chatterjee P, et al. Parameterization of physiologically based biopharmaceutics models: workshop summary report. Mol Pharm. 2024; 21(8): 3697-3731. DOI: 10.1021/acs.molpharmaceut.4c00526
Anklam E, Bahl MI, Ball R, Beger RD, Cohen J, Fitzpatrick S, et al. Emerging technologies and their impact on regulatory science. Exp Biol Med. 2022; 247(1): 1-75. DOI: 10.1177/15353702211052280
Dirnagl U, Bannach-Brown A, and McCann S. External validity in translational biomedicine: understanding the conditions enabling the cause to have an effect. EMBO Mol Med. 2022; 14(2): e14334. DOI: 10.15252/emmm.202114334
Ferrante di Ruffano L, Dinnes J, Sitch AJ, Hyde C, and Deeks JJ. Test-treatment RCTs are susceptible to bias: A review of the methodological quality of randomized trials that evaluate diagnostic tests. BMC Med Res Methodol. 2017; 17(1): 35. DOI: 10.1186/s12874-016-0287-z
Forstmeier W, Wagenmakers EJ, and Parker TH. Detecting and avoiding likely false-positive findings: A practical guide. Biol Rev Camb Philos Soc. 2017; 92(4): 1941-1968. DOI: 10.1111/brv.12315
Parnham MJ, and Geisslinger G. Pharmacological plasticity: How do you hit a moving target?. Pharmacol Res Perspect. 2019; 7(6): e00532. DOI: 10.1002/prp2.532
Frühwein H, and Paul NW. Lost in translation? Animal research in the era of precision medicine. J Transl Med. 2025; 23(1): 152. DOI: 10.1186/s12967-025-06084-3
Cheng H, Deng X, Li J, Tang Y, Yuan S, Huang X, et al. Associations between dysphagia and adverse health outcomes in older adults with dementia in intensive care units: A retrospective cohort study. Clin Interv Aging. 2023; 18: 1233-1248. DOI: 10.2147/CIA.S409828
Brookhart MA, Sturmer T, Glynn RJ, Rassen J, and Schneeweiss S. Confounding control in healthcare database research: Challenges and potential approaches. Med Care. 2010; 48(6): S114-S120. DOI: 10.1097/MLR.0b013e3181dbebe3
Bannach-Brown A, Hair K, Bahor Z, Soliman N, Macleod M, and Liao J. Technological advances in preclinical meta-research. BMJ Open Sci. 2021; 5(1): e100131. DOI: 10.1136/bmjos-2020-100131
Ruckrich S, Agranati G, and Grobman YJ. Earth-based additive manufacturing: A field-oriented methodology for evaluating material printability. Archit Sci Rev. 2023; 66(2): 133-143. DOI: 10.1080/00038628.2022.2154739
World health organization (WHO). Whole genome sequencing as a tool to strengthen foodborne disease surveillance and response: Module 3: Whole genome sequencing in foodborne disease routine surveillance. Geneva: World Health Organization; 2023. Available at: https://www.who.int/publications/i/item/9789240021266
Jarraya I, Abdelkader M, Gabr K, Kadri MB, Alahmed F, Boulila W, et al. OS-RFODG: Open-source ROS2 framework for outdoor UAV dataset generation. Results Eng. 2025; 27: 106613. DOI: 10.1016/j.rineng.2025.106613
Tipton R. The Routledge guide to teaching ethics in translation and interpreting education. Abingdon: Routledge; 2024. DOI: 10.4324/9781003098485
Jarvis MF, and Williams M. Irreproducibility in preclinical biomedical research: Perceptions, uncertainties, and knowledge gaps. Trends Pharmacol Sci. 2016; 37(4): 290-302. DOI: 10.1016/j.tips.2015.12.001
Sessler DI, and Imrey PB. Clinical research methodology 1: Study designs and methodologic sources of error. Anesth Analg. 2015; 121(4): 1034-1042. DOI: 10.1213/ANE.0000000000000815
Serdar CC, Cihan M, Yucel D, and Serdar MA. Sample size, power and effect size revisited: Simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochem Med. 2021; 31(1): 27-53. DOI: 10.11613/BM.2021.010502
Gaskill BN, and Garner JP. Power to the people: Power, negative results and sample size. J Am Assoc Lab Anim Sci. 2020; 59(1): 9-16. DOI: 10.30802/AALAS-JAALAS-19-000042
Eling N, Morgan MD, and Marioni JC. Challenges in measuring and understanding biological noise. Nat Rev Genet. 2019; 20(9): 536-548. DOI: 10.1038/s41576-019-0130-6
Ellis RJ. Questionable research practices, low statistical power, and other obstacles to replicability: Why preclinical neuroscience research would benefit from registered reports. eNeuro. 2022; 9(4): ENEURO.0017-22. 2022. DOI: 10.1523/ENEURO.0017-22.2022
Zhang C, Chen Y, Long Y, Zheng H, Jing J, and Pan W. Helicobacter pylori and gastrointestinal cancers: recent advances and controversies. Clin Med Insights Oncol. 2024; 18: 11795549241234637. DOI: 10.1177/11795549241234637
Spieth PM, Kubasch AS, Penzlin AI, Illigens BMW, Barlinn K, and Siepmann T. Randomized controlled trials: A matter of design. Neuropsychiatr Dis Treat. 2016; 12: 1341-1349. DOI: 10.2147/NDT.S101938
Percie du Sert N, Alfieri A, Allan SM, Carswell HV, Deuchar GA, Farr TD, et al. The improve guidelines (Ischaemia models: Procedural refinements of in vivo experiments). J Cereb Blood Flow Metab. 2017; 37(11): 3488-3517. DOI: 10.1177/0271678X17709185
Alribi AOM, Moafa AAJ, Khairy IA, Zakri HA, Anbari AG, Al Sherif HR, et al. Medical lab technician specialists: Guardians of diagnostic accuracy. Power Syst Technol. 2024; 48(4): 679-691. Available at: https://powertechjournal.com/index.php/journal/article/view/1021/675
Mao Z. Evaluation and impact of measurement uncertainty in laboratory instruments and analytical methods: A comprehensive study of volumetric, chromatographic, and gravimetric techniques. Int J Chem Mater Sci. 2024; 1(1): 1-20. DOI: 10.70088/03hx0b14
Patchipala SG. Tackling data and model drift in AI: Strategies for maintaining accuracy during ML model inference. Int J Sci Res Arch. 2023; 10(2): 1198-1209. DOI: 10.30574/ijsra.2023.10.2.0855
Mays JA, and Mathias PC. Measuring the rate of manual transcription error in outpatient point-of-care testing. J Am Med Inform Assoc. 2019; 26(3): 269-272. DOI: 10.1093/jamia/ocy170
Errington TM, Mathur M, Soderberg CK, Denis A, Perfito N, Iorns E, et al. Investigating the replicability of preclinical cancer biology. eLife. 2021; 10: e71601. DOI: 10.7554/eLife.71601
Zhu L, Tang M, Cai Y, and Wang P. Association between exposure to environmental pollutants and increased oral health risks: A comprehensive review. Front Public Health. 2025; 12: 1482991. DOI: 10.3389/fpubh.2024.1482991
Feldman R. The cancer curse: Regulatory failure by success. Colum Sci Tech L Rev. 2019; 21:1-37. Available at: https://heinonline.org/HOL/LandingPage?handle=hein.journals/cstlr21&div=5&id=&page=
Roy S, Mandal B, and Das BK. Quality assurance in laboratory practices and equipment maintenance: Ensuring precision, reliability, and compliance. In: Das BK, and Kumar V, editors. Laboratory techniques for fish disease diagnosis. Springer, 2025. p. 79. DOI: 10.1007/978-981-96-4620-3_5
Banerjee S, Banerjee D, Pooja D, Kulhari H, Jadhav H, Saharan VA, et al. Unethical practices in research designs. Principles of research methodology and ethics in pharmaceutical sciences. 1st ed. Boca Raton: CRC Press; 2024. p. 47-80. DOI: 10.1201/9781003088226-2
Pamies D, Bal-Price A, Simeonov A, Tagle D, Allen D, Gerhold D, et al. Good cell culture practice for stem cells and stem-cell-derived models. ALTEX. 2017; 34(1): 95-132. DOI: 10.14573/altex.1607121
Rapaport D, Arama ML, and Harlev M. Challenges in maintaining microbial status in specific pathogen-free (SPF) and conventional animal housing. Isr J Vet Med. 2025; 80(1): 1-12. Available at: https://www.ijvm.org.il/node/834
Pearson JA, Tai N, Ekanayake-Alper DK, Peng J, Hu Y, Hager K, et al. Norovirus changes susceptibility to type 1 diabetes by altering intestinal microbiota and immune cell functions. Front Immunol. 2019; 10: 2654. DOI: 10.3389/fimmu.2019.02654
Buchheister S, and Bleich A. Health monitoring of laboratory rodent colonies: Talking about Ievolution. Animals. 2021; 11(5): 1410. DOI: 10.3390/ani11051410
Ratajczak P, Martynski J, Zieba JK, Swiło K, Kopciuch D, Paczkowska A, et al. Comparative efficacy of animal depression models and antidepressant treatment: A systematic review and meta-analysis. Pharmaceutics. 2024; 16(9): 1144. DOI: 10.3390/pharmaceutics16091144
Pellizzon MA, and Ricci MR. Choice of laboratory rodent diet may confound data interpretation and reproducibility. Curr Dev Nutr. 2020; 4(4): nzaa031. DOI: 10.1093/cdn/nzaa031
Messina M, Mejia SB, Cassidy A, Duncan A, Kurzer M, Nagato C, et al. Neither soyfoods nor isoflavones warrant classification as endocrine disruptors: A technical review of the observational and clinical data. Crit Rev Food Sci Nutr. 2022; 62(21): 5824-5885. DOI: 10.1080/10408398.2021.1895054
Kurtz DM, and Feeney WP. The influence of feed and drinking water on terrestrial animal research and study replicability. ILAR J. 2019; 60(2): 175-196. DOI: 10.1093/ilar/ilaa012
Molot J, Sears M, Marshall LM, Bray RI. Neurological susceptibility to environmental exposures: Pathophysiological mechanisms in neurodegeneration and multiple chemical sensitivity. Rev Environ Health. 2022; 37(4): 509-530. DOI: 10.1515/reveh-2021-0043
Mohan GH, Shwetha Reddy RV, and Yogesh C. Management of specific pathogen-free (SPF) Mice and rats. In: Nagarajan P, Gudde R, and Srinivasan R, editors. Essentials of laboratory animal science: Principles and practices. Singapore: Springer; 2021. p. 633-653. DOI: 10.1007/978-981-16-0987-9_26
Tamene A, Afework A, and Mebratu L. A qualitative study of barriers to personal protective equipment use among laundry workers in government hospitals, Hawassa, Ethiopia. J Environ Public Health. 2020: 5146786. DOI: 10.1155/2020/5146786
Schupack DA, Mars RAT, Voelker DH, Abeykoon JP, and Kashyap PC. The promise of the gut microbiome as part of individualized treatment strategies. Nat Rev Gastroenterol Hepatol. 2022; 19(1): 7-25. DOI: 10.1038/s41575-021-00499-1
Ogwu MC. Science and theory of pollution: Sources, pathways, effects and pollution credit. In: Ogwu MC, Chibueze Izah S, editors. Evaluating environmental processes and technologies. Environmental Science and Engineering. Cham: Springer; 2025. p. 117-147. DOI: 10.1007/978-3-031-85327-2_5
Shi Y, Ma J, Li S, Liu C, Liu Y, Chen J, et al. Sex difference in human diseases: Mechanistic insights and clinical implications. Signal Transduct Target Ther. 2024; 9(1): 238. DOI: 10.1038/s41392-024-01929-7
Gossel TA. Principles of clinical toxicology. 3rd ed. CRC Press, 2001. DOI: 10.1201/9780203742167
Qasem RJ. The estrogenic activity of resveratrol: A comprehensive review of in vitro and in vivo evidence and the potential for endocrine disruption. Crit Rev Toxicol. 2020; 50(5): 439-462. DOI: 10.1080/10408444.2020.1762538
Ateia M, Wei H, and Andreescu S. Sensors for emerging water contaminants: Overcoming roadblocks to innovation. Environ Sci Technol. 2024; 58(6): 2636-2651. DOI: 10.1021/acs.est.3c09889
Seyhan AA. Lost in translation: The valley of death across preclinical and clinical divide-identification of problems and overcoming obstacles. Transl Med Commun. 2019; 4(1): 1-19. DOI: 10.1186/s41231-019-0050-7
Collett SR, Smith JA, Boulianne M, Owen RL, Gingerich E, Singer RS, et al. Principles of disease prevention, diagnosis, and control. In: Swayne DE, Boulianne M, Logue CM, McDougald LR, Nair V, Suarez DL, de Wit S, Grimes T, Johnson D, Kromm M, Prajitno TY, Rubinoff I, and Zavala G, editors. Diseases of poultry. 14th ed. John Wiley & Sons, Inc., 2020. p. 1-78. DOI: 10.1002/9781119371199.ch1
Morton LD, Sanders M, Reagan WJ, Crabbs TA, McVean M, and Funk KA. Confounding factors in the interpretation of preclinical studies. Int J Toxicol. 2019; 38(3): 228-234. DOI: 10.1177/1091581819837157
Hirsch C, and Schildknecht S. In vitro research reproducibility: Keeping up high standards. Front Pharmacol. 2019; 10: 1484. DOI: 10.3389/fphar.2019.01484
Turner JG, and Manker JR. Noise as an extrinsic variable in the animal research facility. J Am Assoc Lab Anim Sci. 2024; 63(3): 209-220. DOI: 10.30802/AALAS-JAALAS-24-000008
Meléndez-Fernández OH, Liu JA, and Nelson RJ. Circadian rhythms disrupted by light at night and mistimed food intake alter hormonal rhythms and metabolism. Int J Mol Sci. 2023; 24(4): 3392. DOI: 10.3390/ijms24043392
Mehrotra A, Shukla SP, Shukla AK, Manar MK, Singh SK, and Mehrotra M. A comprehensive review of auditory and non-auditory effects of noise on human health. Noise Health. 2024; 26(121): 59-69. DOI: 10.4103/nah.nah_124_23
Zhang M, Ghosh S, Li M, Altan-Bonnet N, and Shuai D. Vesicle-Cloaked rotavirus clusters are environmentally persistent and resistant to free chlorine disinfection. Environ Sci Technol. 2022; 56(12): 8475-8484. DOI: 10.1021/acs.est.2c00732
Tipton MJ, Harper A, Paton JFR, and Costello JT. The human ventilatory response to stress: Rate or depth?. J Physiol. 2017; 595(17): 5729-5752. DOI: 10.1113/JP274596
Broderick D, Marsh R, Waite D, Pillarisetti N, Chang AB, and Taylor MW. Realising respiratory microbiomic meta-analyses: Time for tandardizedsed framework. Microbiome. 2023; 11(1): 57. DOI: 10.1186/s40168-023-01499-w
Jolles JW, King AJ, and Killen SS. The role of individual heterogeneity in collective animal behaviour. Trends Ecol Evol. 2020; 35(3): 278-291. DOI: 10.1016/j.tree.2019.11.001
Partridge L, Fuentealba M, and Kennedy BK. The quest to slow ageing through drug discovery. Nat Rev Drug Discov. 2020; 19(8): 513-532. DOI: 10.1038/s41573-020-0067-7
Nunamaker EA, and Turner PV. Unmasking the adverse impacts of sex bias on science and research animal welfare. Animals. 2023; 13(17): 2792. DOI: 10.3390/ani13172792
Sapontzis SF. Handling, restraint, and common sampling and administration techniques in laboratory species. The experimental animal in biomedical research. Boca Raton: CRC Press; 2024. p. 333-362. DOI: 10.1201/9781003574446-27
Dowd WW, and Denny MW. A series of unfortunate events: Characterizing the contingent nature of physiological extremes using long-term environmental records. Proc R Soc B. 2020; 287(1918): 20192333. DOI: 10.1098/rspb.2019.2333
Couto M, and Cates C. Laboratory guidelines for animal care. In: Pelegri F, editor. Vertebrate embryogenesis. Methods in molecular biology. Humana, New York: Springer; 2019. p. 407-430. DOI: 10.1007/978-1-4939-9009-2_25
Schuessler B. Thinking outside the conditioning box: Ethological paradigms for studying fear, anxiety and risky decision-making in rodents. Seattle: University of Washington; 2022. Available at: https://digital.lib.washington.edu/server/api/core/bitstreams/0471795f-25d7-41e7-a03a-51c5ad1685e4/content
Ferguson JF, Allayee H, Gerszten RE, Ideraabdullah F, Kris-Etherton PM, Ordovás JM, et al. Nutrigenomics, the microbiome, and gene-environment interactions: New directions in cardiovascular disease research, prevention, and treatment: A scientific statement from the American Heart Association. Circ Cardiovasc Genet. 2016; 9(3): 291-313. DOI: 10.1161/HCG.0000000000000030
Hankenson FC, Marx JO, Gordon CJ, and David JM. Effects of rodent thermoregulation on animal models in the research environment. Comp Med. 2018; 68(6): 425-438. DOI: 10.30802/AALAS-CM-18-000049
Rubio WB, Cortopassi MD, Ramachandran D, Walker SJ, Balough EM, Wang J, et al. Not so fast: Paradoxically increased variability in the glucose tolerance test due to food withdrawal in continuous glucose-monitored mice. Mol Metab. 2023; 77: 101795. DOI: 10.1016/j.molmet.2023.101795
Mische SM, Fisher NC, Meyn SM, Sol-Church K, Hegstad-Davies RL, Weis-Garcia F, et al. A review of the scientific rigor, reproducibility, and transparency studies conducted by the ABRF research groups. J Biomol Tech. 2020; 31(1): 11-26. DOI: 10.7171/jbt.20-3101-003
Amorim AMB, Piochi LF, Gaspar AT, Preto AJ, Rosario-Ferreira N, and Moreira IS. Advancing drug safety in drug development: Bridging computational predictions for enhanced toxicity prediction. Chem Res Toxicol. 2024; 37(6): 827-849. DOI: 10.1021/acs.chemrestox.3c00352
Bailey J. Does the stress of laboratory life and experimentation on animals adversely affect research data? A critical review. Altern Lab Anim. 2018; 46(5): 291-305. DOI: 10.1177/026119291804600501
Lawal O, Elechi K, Adekunle F, Farinde O, Kolapo T, Igbokwe C, et al. A review on artificial intelligence and point-of-care diagnostics to combat antimicrobial resistance in resource-limited healthcare settings like Nigeria. J Pharma Insights Res. 2025; 3(2): 166-175. DOI: 10.69613/reeh4906
Johnson JL, Adkins D, and Chauvin S. A review of the quality indicators of rigor in qualitative research. Am J Pharm Educ. 2020; 84(1): 7120. DOI: 10.5688/ajpe7120
Islam S, and Khan MNI. Project impact assessment frameworks in nonprofit development: A review of case studies from South Asia. Am J Scholarly Res Innov. 2022; 1(1): 270-294. DOI: 10.63125/eeja0t77
Forger DB. Biological clocks, rhythms, and oscillations: The theory of biological timekeeping. Cambridge: MIT Press; 2017.
Dallmann R, Okyar A, and Lévi F. Dosing-time makes the poison: Circadian regulation and pharmacotherapy. Trends Mol Med. 2016; 22(5): 430-445. DOI: 10.1016/j.molmed.2016.03.004
Kolstrup SH, Hansen AK, Pedersen TB. Health management and monitoring. In: Guillén J, and Galligioni V, editors. Practical management of research animal care and use programs. Laboratory animal science and medicine. Springer, 2024. DOI: 10.1007/978-3-031-65414-5_8
Agarwal A. Alternative investments technology: Bridging the gap. 1st ed. Boca Raton: CRC Press; 2025. DOI: 10.1201/9781003481652-2
Kadidlo DM. Staffing, training, and competency. In: Gee AP, editor. Cell therapy. Springer International Publishing AG, 2022. p. 443-458. DOI: 10.1007/978-3-030-75537-9_28
Kwee CM, van Gerven JM, Bongaerts FL, Cath DC, Jacobs G, Baas JMP, et al. Cannabidiol in clinical and preclinical anxiety research: A systematic review into concentration–effect relations using the IB-de-risk tool. J Psychopharmacol. 2022; 36(12): 1299-1314. DOI: 10.1177/02698811221124792
Selvam AP, and Al-Humairi SNS. Environmental impact evaluation using smart real-time weather monitoring systems: A systematic review. Innov Infrastruct Solut. 2025; 10(1): 1-24. DOI: 10.1007/s41062-024-01817-7
Wilson E, Ramage FJ, Wever KE, Sena ES, Macleod MR, and Currie GL. Designing, conducting, and reporting reproducible animal experiments. J Endocrinol. 2023; 258(1): e220330. DOI: 10.1530/JOE-22-0330
Cait J, Cait A, Scott RW, Winder CB, and Mason GJ. Conventional laboratory housing increases morbidity and mortality in research rodents: Results of a meta-analysis. BMC Biol. 2022; 20(1): 15. DOI: 10.1186/s12915-021-01184-0
Voelkl B, Altman NS, Forsman A, Forstmeier W, Gurevitch J, Jaric I, et al. Reproducibility of animal research in light of biological variation. Nat Rev Neurosci. 2020; 21(7): 3843-93. DOI: 10.1038/s41583-020-0313-3
Ruuskanen S. Early-life environmental effects on birds: epigenetics and microbiome as mechanisms underlying long-lasting phenotypic changes. J Exp Biol. 2024; 227(Suppl_1): jeb246024. DOI: 10.1242/jeb.246024
Grieco F, Bernstein BJ, Biemans B, Bikovski L, Burnett CJ, Cushman JD, et al. Measuring behavior in the home cage: Study design, applications, challenges, and perspectives. Front Behav Neurosci. 2021; 15: 735387. DOI: 10.3389/fnbeh.2021.735387
Cunha-Oliveira T, Ioannidis JPA, and Oliveira PJ. Best practices for data management and sharing in experimental biomedical research. Physiol Rev. 2024; 104(3): 1387-1408. DOI: 10.1152/physrev.00043.2023
Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. J Cereb Blood Flow Metab. 2020; 40(9): 1769-1777. DOI: 10.1177/0271678X20943823
Subha S, Shanmugathai M, Prasanth A, Varagi SS, and Dhanashree V. Digital transformation in the pharmaceutical and biotech industry: Challenges and research directions. In: Dhanaraj RK, Balusamy B, Samuel P, Bashir AK, and Kadry S, editors. Digital twins in industrial production and smart manufacturing: an understanding of principles, enhancers, and obstacles. Hoboken: Wiley-IEEE Press; 2024. p. 297-324. DOI: 10.1002/9781394195336.ch13
Zheng F, Xiao Y, Liu H, Fan Y, and Dao M. Patient-specific organoid and organ-on-a-chip: 3D cell-culture meets 3D printing and numerical simulation. Adv Biol. 2021; 5(6): e2000024. DOI: 10.1002/adbi.202000024