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BioCorteX takes a unique approach to therapeutics by considering three specific interactions between bacteria: the direct effect of drug-bacteria metabolism, the indirect effect of bacteria and the host physiology, and the local effects on specific tissues, like the tumour microbiome.
Pharmaceuticals
In modern drug development, the makeup of an individual’s bacteria is a complex and expensive problem. The possible bacterial permutations that need to be analysed are almost infinite and performing the necessary analysis takes exceptional computational and financial resources.
As a result, the magnitude of diversity in the human microbiome is overlooked, leading to costly clinical trial failures and the continued use of medicines with varying levels of response.
At BioCorteX our foundational emulator has the ability to conduct advanced computational scenarios that model complex human responses. This provides the BioCorteX team and partners with the opportunity to test drug-bacteria interactions time and time again, in silico.
We have conducted in silico trials of hundreds of medications and assets in development to understand the factors that determine success.
By providing actionable insights at any stage of drug development BioCorteX can de-risk clinical trials, increase chances of trial success and improve internal rates of return, while providing better outcomes for people worldwide.
Antibody-drug conjugates (ADCs) are transforming cancer care by delivering potent, targeted chemotherapy to tumours. They minimise harm to healthy tissue offering significant benefits to patients. With the global ADC market expected to reach tens of billions of dollars, the stakes – and the risks – are substantial. We illustrate the reality of these risks through two focussed scenarios.
Each ADC costs hundreds of millions to develop, and a single failed trial can mean billions lost in R&D and market opportunities. Despite hundreds of development efforts, only 13 ADCs have received FDA approval, highlighting an urgent need to improve clinical trial success rates.
We believe that consistent ADC success is achievable – and it hinges on a deep understanding of the ubiquitous yet highly variable ADC – bacteria interactions within tumour microenvironments (TMEs).
Trial success or failure can depend on ADC – bacteria interactions (See the example below). ADC – bacteria interactions are pervasive in today’s drug development pipelines, influencing clinical outcomes through mechanisms such as target availability, payload release, and linker modulation.
Example in silico trial
Company: Publicly listed large pharmaceutical company.
Asset: ADC*. Assumed effect size: HR = 0.51 (Cox proportional hazards vs indication matched standard-of-care).
Interaction characteristics: Intra-tumour bacteria – ADC interaction. Advanced solid tumour indication*, geography-specific regulatory jurisdiction *.
Specific mechanism: Confidential
Input data: Primary tumour sequencing data ✝︎ BioCorteX: Real-world survival data.
*Parameters that can be customised to client specifications
✝︎ Only raw FASTQ files are required. No tissue provision is necessary.
Randomised control (random presence of bacteria) confirmed alignment with the baseline group, reinforcing the relevance of the ADC – specific interactions.
Likely Outcome: Trial Failure.
Based on estimates, for a typical Phase II clinical trial, only one in fifteen trials would randomly negate the variability introduced by ADC – bacteria interactions.
Stratification based on ADC – Bacteria interactions.
Likely Outcome: Trial Success
BioCorteX ADC – Bacteria biomarker likely results in a successful trial with a 30% increase in overall survival (OS) rate in silico compared to the baseline treatment population without a biomarker selection.
We can increase the probability of success (POS) by optimising clinical trial site selection, choice of cancer type, stage, and molecular subtyping criteria.
We can also help when moving assets between geographical regions, such as China to Global, or Japan to Global.
“If you are being chased by a bear, you don’t have to be faster than the bear, just faster than the slowest person.”
Limitations: These findings are based on emulated data rather than prospective clinical trials. Our in silico models provide valuable predictions, but they may not fully capture the complexities of real patient populations.
Disclaimer: The information provided by BioCorteX Inc. is for general informational purposes only and should not be construed as medical advice. The content is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Any mention of specific treatments is for informational purposes only and does not constitute an endorsement or recommendation by BioCorteX. BioCorteX is not responsible for any errors or omissions or any actions taken based on the information provided.
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