After years of drought, recent technological and clinical breakthroughs are causing research into chronic kidney disease treatments to resurge.
Chronic kidney disease is an incurable condition where the kidneys are damaged over time and gradually stop working properly. It usually overlaps with common age-related conditions such as cardiovascular disease and diabetes.
In spite of a growing market, there are still no effective treatments for chronic kidney disease. Current medications are limited to tackling the symptoms, which include swelling and blood in the urine. Even this much is a struggle for patients suffering from multiple conditions and taking lots of different medications, which can further damage the kidneys.
“While many of the symptoms can be controlled successfully, in many cases, the underlying condition will continue to progress, causing people living with chronic kidney disease to require dialysis and ultimately a kidney transplant,” said Uwe Andag, Head of Metabolic Diseases at the German drug development firm Evotec.
Why are there no effective treatments? One big reason is a historical lack of biomarkers that can easily diagnose the disease. Without reliable diagnostics, it is tough to tell how well a drug works, and also to select patients for clinical trials.
“Chronic kidney disease is a very diverse disease … and still there is a huge percentage of patients — approximately one in three — that can’t be classified based on current standard diagnostics,” said Andag.
Past clinical failures in chronic kidney disease from big drug developers such as Roche and AbbVie haven’t helped either. One major example was a type 2 diabetes treatment developed by Sanofi and the US-based company Lexicon Pharmaceuticals. The drug had disappointing phase III results in patients with both type 2 diabetes and chronic kidney disease, leading Sanofi to walk out of the collaboration last year.
“One very likely reason for these negative study outcomes is the lack of translation from rodents to human patients. ‘We have cured so many mice, but never a human patient,’” said Andag.
It’s even got to the point where clinical trials that test drugs for cardiovascular disease, diabetes, and cancer have often excluded patients also suffering from chronic kidney disease. As a result, these patients tend to miss out on the latest treatments.
This year, however, the winds seem to be changing, with positive clinical results buoying pharma companies’ efforts. “Indeed, there seems to be a renaissance in the field,” said Andag.
Last week, for example, a class of drugs called SGLT2 inhibitors, which increase the amount of blood sugar passing into the urine, slowed down chronic kidney disease in phase III trials. In July, a drug developed by Bayer to block steroid hormones also delayed kidney failure in phase III.
“These arising potential medications would still not cover all chronic kidney disease etiologies and therefore still leave many patients with little-to-no treatment options,” added Andag.
Pharma is often turning to biotech to fill in the gaps. In June, the big pharma Novo Nordisk snapped up Corvidia Therapeutics in the US, which is developing a drug to treat cardiovascular disease in patients with chronic kidney disease. Novo Nordisk then crafted a drug discovery deal with Evotec to treat chronic kidney disease. At the same time, AstraZeneca teamed up with the UK firm RenalytixAI to boost precision medicine in the condition using artificial intelligence (AI).
What are the main drivers of this chronic kidney disease renaissance? “Increased research in the area is now leading to further understanding of drivers of disease,” said Karin Conde-Knape, Corporate Vice President of Diabetes, Cardio-renal and Translational Research at Novo Nordisk. She also told me that more is known about the disease than before because companies have larger amounts of disease data available from patients.
The partnership between Novo Nordisk and Evotec will rely heavily on patient data to unpick the complexities of chronic kidney disease. Specifically, the two companies will pool their databases containing information such as clinical outcomes and genomics. Then they will use the information to stratify patients, make drug discovery models, and eventually develop drug candidates.
“We believe this is perhaps the world’s most comprehensive database in this field,” said Andag.
The approach will be ‘modality-agnostic,’ so, at the moment, it’s unclear what types of drugs will emerge from the collaboration.
Meanwhile, AstraZeneca and its partner RenalytixAI — recently flushed with €63M in IPO cash — will deploy AI to analyze biomarkers and other diagnostic information in patients with chronic kidney disease. RenalytixAI’s technology can predict which patients have the highest risk of kidney failure, and those that might best respond to AstraZeneca’s drug candidates in a clinical trial.
“We believe this collaboration will define how we can leverage [AI technology] to improve the care and outcomes for patients affected by chronic diseases, such as kidney disease, diabetes, and cardiovascular disease,” stated Barbara Murphy, a board member of RenalytixAI. “By using a more personalized approach, our initial goal is to help realize improved outcomes for more than 240,000 patients with chronic kidney disease.”
For patients that need kidney transplants, more treatments are opening up this year. Last week, for instance, the EU approved a drug developed by the Swedish company Hansa Biopharma that could reduce the likelihood of kidney transplant rejections by the immune system.
Overall, this year is marking a positive swing for patients with chronic kidney disease, who have lacked effective treatments for so long. And this could continue to bring in more funding for biotechs in this field going forward.
“Now, with access to human data and samples as well as new technologies allowing in-depth analysis of such, but also recent positive outcomes in chronic kidney disease trials, interest and investment by pharma and biotech is back,” concluded Andag.
Images from E. Resko and Shutterstock
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