Our Vision

Cardiorenal QSP aims to be the leading drug-development partner for cardiorenal mechanistic modeling. We believe precision models, when grounded in our best understanding of physiology and decades of experimental and clinical data, can bridge translational gaps and reveal effects that trial data alone may miss.

What Makes Us Unique

Deep Kidney Expertise

16+ years modeling kidney physiology
Deep QSP focus on kidney, integrated with cardiac & vascular systems
Core models are peer reviewed and continuously validated

Unmatched Mechanistic Modeling

Multi-scale modeling from molecular mechanisms to whole-organ physiology
Links PK-PD to clinical biomarkers via hormones, transport, hemodynamics, and flow dynamics

Proven Impact in Drug Development

Long-term pharma collaborations with proven success
We understand timelines, regulatory expectations, and the practical needs of R&D teams

K. Melissa Hallow, Ph.D.

Founder & Principal Consultant

Melissa Hallow is a leading expert in Cardiorenal Quantitative Systems Pharmacology (QSP) with 16+ years of experience helping pharmaceutical companies integrate mechanistic knowledge and data to drive more informed decisions.

She earned her PhD in Mechanical Engineering from Georgia Tech and began her career in QSP modeling at Novartis. She then spent 11 years on faculty in the College of Engineering at the University of Georgia, where she led a research group that developed, validated, and published state-of-the-art models of cardiorenal function. She has collaborated with pharmaceutical partners to apply these models across the drug development pipeline - from early development to post-marketing.

Melissa is recognized internationally for her contributions to the field and was honored as the State-of-the-Art Keynote Lecturer at the American Conference on Pharmacometrics (ACOP). Through Cardiorenal QSP, LLC, Melissa now partners directly with companies to accelerate development of therapies targeting complex cardiorenal mechanisms.

Key Experience

PhD in Mechanical Engineering, Georgia Tech
16+ years in pharmaceutical modeling and simulation
11 years leading academic research in cardiorenal modeling
Expert in Cardiorenal Drug Development and mechanistic modeling

Building on a Legacy of Scientific Discovery

Our modeling platforms stand on the shoulders of decades of computational, experimental, and clinical research. The insights we provide today are made possible by the painstaking work of countless dedicated scientists who have advanced our understanding of renal and cardiovascular physiology.

Scientific Foundations

Academic Modelers

These computational scientists paved the way for the models we use.

Arthur C. Guyton

Physiologist and Pioneer of Mathematical Modeling

Key Publication: Guyton, A. C., et al. (1972). "Circulation: Overall Regulation" Annual Review of Physiology

F. Karaaslan

Computational Physiologist

Key Publication: Karaaslan F, Denizhan Y, Kayserilioglu A, Gulcur HO. Long-term mathematical model involving renal sympathetic nerve activity, arterial pressure, and sodium excretion. Ann Biomed Eng. 2005 Nov;33(11):1607-30. doi: 10.1007/s10439-005-5976-4. PMID: 16341927.

P. Bovendeerd & T. Arts

Cardiovascular Modelers

Key Publication: Bovendeerd, P. H., Borsje, P., Arts, T., and van, D. V. (2006). Dependence of intramyocardial pressure and coronary flow on ventricular loading and contractility: a model study. Ann. Biomed. Eng. 34, 1833–1845. doi: 10.1007/s10439-006-9189-2

A. Weinstein

Renal Physiologist

Key Publication: Weinstein, A., et al. (2007). "Mathematical model of renal function" Journal of Mathematical Biology

MJ Lazzara & WM Deen

Renal Transport Modelers

Key Publication: Lazzara MJ, Deen WM. Model of albumin reabsorption in the proximal tubule. Am J Physiol Renal Physiol. 2007 Jan;292(1):F430-9. doi: 10.1152/ajprenal.00010.2006. Epub 2006 Sep 5. PMID: 16954345.

Salem & Cabrera

Cardiac Metabolism Modelers

Key Publication: Salem, J.E., Saidel, G.M., Stanley, W.C. et al. Mechanistic Model of Myocardial Energy Metabolism Under Normal and Ischemic Conditions. Annals of Biomedical Engineering 30, 202–216 (2002). https://doi.org/10.1114/1.1454133

Cardiorenal Modeling Community

Anita Layton, Robert Hester, Aurelie Edwards, John Osborn, John Clemmer, Owen Richfield, and others

Physiologists and Clinical Scientists

This is an incomplete list of scientists whose experimental and clinical studies inform our models.

Physiologists:

Peter Bie, Arthur Guyton, Barry Brenner, John Hall, JP Granger, Roland Blantz, Scott Thomson, Donald Kohan, Gabriel Navar, Jennifer Pollock, GL Bakris, Olga Schmidlin, RC Morris, T Kurtz, JN Bech, CB Nielsen, EB Petersen, RG Dluhy, GH Williams, B Dussol, Christopher Wilcox, TN Acosta-Barrios, GT McInnes, Chris Baylis, Kirk Conrad, J Nussberger, HR Brunner, M Burnier, GA MacGregor, Y Aito, K Nakao, H Imura, AJ Reyes, WP Leary, K Yamamoto, CM Ferrario

Clinical Scientists:

Hiddo Lambers-Heerspink, Barry Borlaug, Margaret Redfield, Michael Brands, John McMurray, Jonathan Murrow, Milton Packer, Scott Solomon, Marc A. Pfeffer, Eugene Braunwald, Peter Rossing

Industry Contributors

Our technology was shaped through deep collaboration with industry scientists, whose contributions were central to the model’s relevance, robustness, and translational value.

Early model development and first publications: Novartis and Entelos

Pharmaceutical sponsors of Academic research: AstraZeneca, Eli Lilly, Takeda, Pfizer, Merck

Companies listed here contributed to the scientific development of the modeling framework. Inclusion does not imply endorsement.

University of Georgia

Key elements of the platform were developed at University of Georgia, with support from:

Institutional research infrastructure
Interdisciplinary academic collaborations
Funded partnerships with industry

This phase enabled the publication of foundational studies in quantitative systems pharmacology and fostered the training of junior researchers whose contributions are reflected in the current model.

Selected Publications

Hallow KM, Greasley PJ, Heerspink HJL, Yu H. Kinetics of endothelin-1 and effect selective ETA antagonism on ETB activation: a mathematical modeling analysis. Front Pharmacol. 2024 Nov 26;15:1332388. doi: 10.3389/fphar.2024.1332388. PMID: 39664514; PMCID: PMC11632605.

Basu, S., Yu, H., Murrow, J. R., & Hallow, K. M. (2023). Understanding heterogeneous mechanisms of heart failure with preserved ejection fraction through cardiorenal mathematical modeling. PLoS Computational Biology, 19(11 November). https://doi.org/10.1371/journal.pcbi.1011598

Hallow, K., Greasley, P., Heerspink, H., & Yu, H. (2023). Endothelin and the role of venous capacitance in fluid retention with endothelin antagonists -- a mathematical modeling analysis. Physiology, 38(S1). https://doi.org/10.1152/physiol.2023.38.s1.5735288

Yu, H., Basu, S., Tang, W., Penland, R. C., Greasley, P. J., Oscarsson, J., Boulton, D. W., & Hallow, K. M. (2022). Predicted Cardiac Functional Responses to Renal Actions of SGLT2i in the DAPACARD Trial Population: A Mathematical Modeling Analysis. Journal of Clinical Pharmacology, 62(4). https://doi.org/10.1002/jcph.1987

Maddah, E., & Hallow, K. M. (2022). A quantitative systems pharmacology model of plasma potassium regulation by the kidney and aldosterone. Journal of Pharmacokinetics and Pharmacodynamics, 49(4). https://doi.org/10.1007/s10928-022-09815-x

Melissa Hallow, K., & Dave, I. (2021). RAAS Blockade and COVID-19: Mechanistic Modeling of Mas and AT1 Receptor Occupancy as Indicators of Pro-Inflammatory and Anti-Inflammatory Balance. Clinical Pharmacology and Therapeutics, 109(4). https://doi.org/10.1002/cpt.2177

Yu, H., Tang, W., Greasley, P. J., Penland, R. C., Boulton, D. W., & Hallow, K. M. (2021). Predicted Cardiac Hemodynamic Consequences of the Renal Actions of SGLT2i in the DAPA-HF Study Population: A Mathematical Modeling Analysis. Journal of Clinical Pharmacology, 61(5). https://doi.org/10.1002/jcph.1769

Hallow, K. M., van Brackle, C. H., Anjum, S., Ermakov, S. (2021). Cardiorenal Systems Modeling: Left Ventricular Hypertrophy and Differential Effects of Antihypertensive Therapies on Hypertrophy Regression. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.679930

Lewis, S., Nieves, E., & Hallow, M. (2021). Quantification of Protein Reabsorption Energetics in the Kidney Proximal Tubule. The FASEB Journal, 35(S1). https://doi.org/10.1096/fasebj.2021.35.s1.02761

Yu, H., & Hallow, M. K. (2020). Evaluation of Natriuretic Control Signals: A Mathematical Modeling Analysis. The FASEB Journal, 34(S1). https://doi.org/10.1096/fasebj.2020.34.s1.06557

Hallow, K. M., Boulton, D. W., Penland, R. C., Helmlinger, G., Nieves, E. H., van Raalte, D. H., Heerspink, H. L., & Greasley, P. J. (2020). Renal effects of dapagliflozin in people with and without diabetes with moderate or severe renal dysfunction: Prospective modeling of an ongoing clinical trial. Journal of Pharmacology and Experimental Therapeutics, 375(1). https://doi.org/10.1124/JPET.120.000040

Mahato, H. S., Ahlstrom, C., Jansson-Löfmark, R., Johansson, U., Helmlinger, G., & Hallow, K. M. (2019). Mathematical model of hemodynamic mechanisms and consequences of glomerular hypertension in diabetic mice. Npj Systems Biology and Applications, 5(1). https://doi.org/10.1038/s41540-018-0077-9

Yu, H., & Hallow, M. K. (2019). Cardiac and Renal Function Interactions in Heart Failure with Reduced Ejection Fraction: A Mathematical Modeling Analysis. The FASEB Journal, 33(S1). https://doi.org/10.1096/fasebj.2019.33.1_supplement.532.15

Basu, S., Boulton, D., & Hallow, M. K. (2019). SGLT2 Inhibition Is Predicted to Reduce LV End Diastolic Pressure: A Mathematical Modeling Analysis. The FASEB Journal, 33(S1). https://doi.org/10.1096/fasebj.2019.33.1_supplement.531.17

Anjum, S., Jajamovich, G., Allen, R., Sher, A., Dockendorf, M., Musante, C. J., & Hallow, K. M. (2018). Mathematical modeling of left ventricle hypertrophy and dilatation in response to volume overload in heart failure: a coupled renal‐cardiac model. The FASEB Journal, 32(S1). https://doi.org/10.1096/fasebj.2018.32.1_supplement.903.22

Gebremichael, Y., Lu, J., Shankaran, H., Helmlinger, G., Mettetal, J., & Hallow, K. M. (2018). Multiscale mathematical model of drug-induced proximal tubule injury: Linking urinary biomarkers to epithelial cell injury and renal dysfunction. Toxicological Sciences, 162(1). https://doi.org/10.1093/toxsci/kfx239

Hallow, K. M., Greasley, P. J., Helmlinger, G., Chu, L., Heerspink, H. J., & Boulton, D. W. (2018). Evaluation of renal and cardiovascular protection mechanisms of SGLT2 inhibitors: Model-based analysis of clinical data. American Journal of Physiology - Renal Physiology, 315(5). https://doi.org/10.1152/ajprenal.00202.2018

Gebremichael, Y., Lu, J., Shankaran, H., Helmlinger, G., Mettetal, J., & Hallow, M. (2017). Hemodynamic Consequences of Cisplatin‐Induced Acute Proximal Tubular Injury: A Mathematical Modeling Analysis. The FASEB Journal, 31(S1). https://doi.org/10.1096/fasebj.31.1_supplement.1030.7

Hallow, K. M., & Gebremichael, Y. (2017a). A quantitative systems physiology model of renal function and blood pressure regulation: Application in salt-sensitive hypertension. CPT: Pharmacometrics and Systems Pharmacology, 6(6). https://doi.org/10.1002/psp4.12177

Hallow, K. M., & Gebremichael, Y. (2017b). A quantitative systems physiology model of renal function and blood pressure regulation: Model description. CPT: Pharmacometrics and Systems Pharmacology, 6(6). https://doi.org/10.1002/psp4.12178

Hallow, K. M., Gebremichael, Y., Helmlinger, G., & Vallon, V. (2017). Primary proximal tubule hyperreabsorption and impaired tubular transport counterregulation determine glomerular hyperfiltration in diabetes: A modeling analysis. American Journal of Physiology - Renal Physiology, 312(5). https://doi.org/10.1152/ajprenal.00497.2016

Hallow, M., Helmlinger, G., & Gebremichael, Y. (2016). Salt Sensitivity and Tubular Mechanisms of Pressure Natriuresis: A Mathematical Modeling Analysis. The FASEB Journal, 30(S1). https://doi.org/10.1096/fasebj.30.1_supplement.lb736

Hallow, M., & Vallon, V. (2016). Mathematical Model‐based Analysis of the Acute and Chronic Mechanisms of Diabetic Hyperfiltration. The FASEB Journal, 30(S1). https://doi.org/10.1096/fasebj.30.1_supplement.740.6

Hallow, K. M. (2015). Mathematical model of renal hemodynamic and structural consequences of increased proximal sodium reabsorption in early diabetic kidney disease. The FASEB Journal, 29(S1). https://doi.org/10.1096/fasebj.29.1_supplement.959.1

Lo, A., Beh, J., de Leon, H., Hallow, M. K., Ramakrishna, R., Rodrigo, M., Sarkar, A., Sarangapani, R., & Georgieva, A. (2011). Using a systems biology approach to explore hypotheses underlying clinical diversity of the renin angiotensin system and the response to antihypertensive therapies. AAPS Advances in the Pharmaceutical Sciences Series, 2011(1). https://doi.org/10.1007/978-1-4419-7415-0_20

In Memoriam

Esteemed Colleague, a Champion of Cardiorenal QSP, and Dear Friend.

Yeshi Gebremichael, PhD

1965 – 2025