The design of new proteins (i.e.Antibodies and Enzymes) with specific function is a very complex problem.
The problem has a great relevance for the pharmaceutical sector and the chemical industries with an expanding market of products and services.
Example of target market: protein therapeutics.
The drug discovery process is characterized by high return of investment and a long, high-cost, low success rate pipeline. To obtain high performance molecule candidates, gives not only advantages against competitors but it reduces cost and waste of resources on later steps.
Patent status
SUBMITTED
Priority Number
102019000009531
Priority Date
19/06/2019
License
ITALY
Market
Sectors:
pharmaceutical, food and detergent industries,
environmental applications, biopolymer production.
In the next future the integration of high-throughput experiments with AI will drive a disruptive change in the R&D of the Pharmaceutical and Biotechnological sector.
Aims:
In a first stage our company aims to help organizations that require the use of data science and advanced analytics in the development of a new product while missing the necessary resources and capabilities.
TAM – total addressable market
Protein engineering market ~ 2 Billions
(Pharmaceutical, food and detergent industries, environmental applications, biopolymer production)
SAM – serviceable addressable market
Protein design sector ~ 700 Millions
SOM – serviceable obtainable market
Pharmaceutical or chemical industry companies with R&D wet labs for protein engineering
Problem
The design of new proteins (i.e.Antibodies and Enzymes) with specific function is a very complex problem.
The problem has a great relevance for the pharmaceutical sector and the chemical industries with an expanding market of products and services.
Example of target market: protein therapeutics.
The drug discovery process is characterized by high return of investment and a long, high-cost, low success rate pipeline. To obtain high performance molecule candidates, gives not only advantages against competitors but it reduces cost and waste of resources on later steps.
Current technologies limits / Solutions
Directed Evolution
One of the most used approaches is the Directed Evolution, which makes it possible to characterize the functionality of combinatorial libraries of mutants with the aim of selecting the variants with an increased functionality of interest.
Limitations:
Innovation trends:
Killer Application
The lead compound identification and optimization are two crucial step in the Drug Discovery process. During these steps the high-throughput screening experiments are routinely carried out to obtain high-performance compounds.
Proteolabio can be used to obtain higher performance molecule candidates: this gives not only advantages against competitors but it reduces cost and waste of resources on the extremely costly later steps of pre-clinical and clinical studies.
Technology and our solution
A computational method for biological sequence optimization.
The present invention uses an innovative machine-learning method which, starting from library sequencing, produces an accurate statistical modeling of the genotype-fitness association, allowing the construction of models for the generation of new amino acid sequences with biochemical functions of interest.
The core service deliverable by our technology consists of the prediction and generation of optimized sequences by exploiting the information of high-throughput screening experiments such as Directed Evolution.
Advantages
Our technology provides high accuracy prediction of mutational effects that can be exploited to computationally explore the sequence space of the target protein. This allows to perform quick and inexpensive probing of new potential candidates, which allows the client to increase the success rate of discovery or optimization process of a product. At the same time it accelerates the process from the laboratory to the market and it reduces the laboratory waste of resources and costs.
Technological advantages
Solutions to market pain
The technological advantages provide:
To give one example of application, in the drug discovery process the lead compound identification and optimization is a crucial step to have valid candidates to enter the extremely costly pre-clinical and clinical studies. To obtain high-performance molecule candidates permits to reduce the cost and waste of resources in later steps of the process.
Roadmap
TRL
The team
