The world’s first transomics drug discovery and development startup

Ayuh Ventures invested in Pepper Bio, whose computational biology platform uses transomics to give researchers access to the most comprehensive understanding of any disease.

The key to Pepper Bio’s platform is the phosphoproteomic data, which other drug discovery companies have been largely unable to leverage. Phosphoproteomics is a type of proteomics that characterizes proteins with the reversible post-translational modification of phosphorylation, which has a vital role in cellular processes such as cell cycle regulation, signal transduction, and protein targeting. It provides insights that other omics miss since change in phosphorylation status almost always reflects a change in protein activity, which indicates what proteins might be potential drug targets. Abnormal protein phosphorylation has been implicated in a number of diseases, notably cancer, but also neurodegenerative diseases like Alzheimer’s Disease and Parkinson’s Disease and inflammatory diseases like Crohn’s Disease and Ulcerative Colitis, among other disorders and disease categories.

Drug Discovery Today

The first step in the drug discovery process is the identification of a disease-modifying target — a key molecule interfering with biological pathways that are specific to a disease or a disease state. This is a critical step that traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular targets to disease, and to evaluate the efficacy, safety and commercial potential of the target. Currently, most drug development is risky (3% probability of success) and costly, but this is because the industry doesn’t fully understand what drives a disease or what a drug does when it goes into a patient. Fundamentally, this stems from three key challenges:

1. Lack of understanding biological activity

a. Many factors influence health and disease. Historically, researchers looked at the presence of a particular molecule to gain insights on what could potentially happen in the body by understanding how much of a particular molecule or molecules are present in a cell, but the important part is knowing what that molecule is actually doing. The presence of a molecule alone may or may not be indicative of disease because a molecule can do many different things or nothing at all. Just knowing the amount of those molecules does not give you information on their activity.

2. Looking in the wrong places

a. Currently, drug developers will pre-select a few narrow regions of biology to probe into by looking at what’s been done before. If the historical knowledge didn’t capture all the relevant receptor/protein locations, then the developers will miss looking into those locations. In many instances, the places they didn’t look at causes the drug to be ineffective or too toxic and drugs will often fail as a result.

3. Correlation is not causation

a. Due to the sheer number of variables when looking at omic data, researchers will inevitably end up identifying many statistically significant results just by chance alone. There are too many results for researchers to follow-up on. Furthermore, even if they could follow-up on all of them, they wouldn’t want to because it’ll take too long and be too expensive.

Due to these key challenges, drug developers are forced to make many assumptions and guesses when designing a drug in its path towards approval. Unfortunately, many of these assumptions and guesses are wrong, which leads to the eventual failure of the program. In recent decades, the use of multi-omics data (genomic, transcriptomic, metabolomic, etc) has resulted in high-throughput screening that, by allowing quantitative measurements of many targets, has exponentially increased the volume of scientific data available. However, human diseases are complex and involve many interrelated pathways, which can lead to the identification of different molecular targets. As a result, multi-omic data has been a key tool employed in driving drug discoveries in recent years due to its ability to uncover optimal drug targets. Data is initially collected from patients and integrated to create their molecular profiles, which are then matched to previously defined disease profiles that can guide the selection of treatment. This is achieved either through a match to known biomarkers, omics signatures or network/pathway signatures. The appropriate molecule is then developed based on this match to improve the chance of successful treatment and reduce the probability of side effects in a certain population — given the $200B+ in annual cancer treatments, there is more than enough TAM in each patient population to create a blockbuster drug.

However, proteins are the major effectors of cell functions through changes in their posttranslational modifications (PTMs) and abundance, reflected also on changes in their interactome with effects on cell phenotypes. Accordingly, several important aspects of the drug discovery process, including target identification, mechanism of action determination and biomarker identification as well as drug repositioning, require complete understanding of the effects of drugs on protein phosphorylation in relevant biological systems. Therefore, it is critical to also consider proteomics and phosphoproteomics along with other omics data to understand disease development and subtypes, as they can better capture the functional state and dynamic properties of a cell in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, signatures, and treatments.

What’s Different About Pepper Bio?

Pepper Bio is the world’s first transomics drug discovery startup that includes genomics, transcriptomics, proteomics, and phosphoproteomics all in one medicine development and discovery platform. Many drug discovery companies have wanted to leverage global phosphoproteomic data, but it’s been impossible to do so until now due to the difficulty in interpreting data — the “dictionary” used to interpret data has historically been limited and does not provide many good insights. Through Pepper’s proprietary technology, their “dictionary” is more than three times as big as the traditional dictionary used — the dictionary defines relationships between analytes, pathways, and clinical outcomes. This allows Pepper to identify actionable insights otherwise missed from a rich dataset of site-specific phosphoproteomic data and the company can design safe, efficacious drugs and select the patients who will respond to the treatment as a result. In doing so, Pepper solves the three key challenges identified above because the transomic layers of their platform provides data that is:

1. Functional

a. Pepper directly identifies the biological activities that are occurring by incorporating phosphoproteomic and proteomic data. This allows them to understand what is happening in the system and not just what’s there.

2. Causal

a. Pepper can identify causal relationships by integrating across the four omic layers (genomic, transcriptomic, proteomic, and phosphoproteomic), enabling them to strip out a lot of the noise in the data. This allows them to craft a comprehensive narrative of biology to understand why things are happening.

With the addition of phosphoproteomics, Pepper’s technology demonstrates the major hallmarks of an optimal drug discovery platform:

1. Target identification

a. Pepper’s technology can identify phosphotargets associated with disease

2. Understanding mechanism of action

a. Pepper’s technology can identify the phosphoproteins that a compound affects in the signaling network of several cell types

3. Signaling network construction

a. Pepper’s technology can compare signaling networks in normal and disease states by using a combination of database knowledge and algorithms developed to construct and compare pathways

4. Compound stratification

a. Pepper’s technology can compare the phosphoprotein signaling of drugs from libraries and connect them to their chemical structures

5. Drug repositioning

a. Pepper’s technology can screen drug libraries and identify drugs that affect phosphoprotein signaling in a similar way to that of approved drugs. Also, use increased understanding of mechanism of action and toxicity to determine if the drug could be repositioned to a different disease

6. Understanding toxicity

a. Pepper’s technology can identify if a new compound is toxic by comparing phosphorylation signatures with compounds with known toxicity profiles

7. Biomarker discovery

a. Pepper’s technology can identify phosphoprotein biomarkers that enable a more accurate monitoring of pharmacodynamics

8. Patient stratification biomarkers

a. Pepper’s technology can profile phosphoprotein states in patients to determine markets that are predictive of patients that will respond to various treatments.

Drug Discovery Landscape

While a number of other omics companies have cropped up in the drug discovery space, Pepper Bio is the only one with the aforementioned dictionary and novel technologies capable of deciphering the mystery of phosphoproteomic data. Phosphoproteomics is vastly different from interpreting the other three omic layers due to how the data is structured. As it turns out, you can apply similar analysis methodology to interpreting genomics, transcriptomics, or proteomics. However, if you use that same approach for phosphoproteomics, you end up drawing poor conclusions from the data. This data layer gives the startup the additional advantage of producing drugs that are more likely to have less toxicity and have higher response rates for patients while exhibiting fewer and less severe side-effects.

One of the biggest risk here is Phosphoproteomics is a relatively new and highly complex data type and as this team continues to adopt new data, they will need to successfully integrate, including future omic layers. Fact is Proteomics and Phosphoproteomics continue to be less researched fields of biology, which makes this research even more exciting.