A bioprocess startup using fly larvae as mini-bioreactors hopes to speed up its protein engineering pipelines by spreading its skilled workforce across two continents.
Proteinea, which joined San Francisco’s IndieBio Accelerator in February 2021, has deployed 15 of its 20-strong teams in Cairo to capitalize on the lower costs of building a talented team.
“We have a bigger team in Egypt than [we could hire] with the same budget in the US, ”explains Mahmoud Eljendy, Co-Founder and CEO of Proteinea. “With a large team at this early stage, we can do more experiments and cycle through our products and business model faster.”
Team members also come from diverse backgrounds, ranging from aerospace engineering to bioinformatics to insect genetics, he says.
Eljendy compares the advantages of the larger, more diverse team to playing Grandmaster Chess – the winning strategy being to make two moves while another player only has one. “Speed is everything,” he says, especially in the fast-moving biotech market. The benefits include allowing more experiments to be conducted at the same time – a traditionally time-consuming part of biotech development.
The company, which has its IndieBio demo day in mid-July, uses the IndieBio lab room to get technology advice, as well as experiments, mentoring and business coaching from the IndieBio team. Their business model is based on using insect larvae as mini-bioreactors, which they hope will reduce the cost of protein production by up to 90%.
As Eljendy explains, fly larvae are easier to scale than with single-use or stainless steel bioreactors because processes in a single larva should be similar in millions of others. In contrast to cells that have been cultured in a bioreactor, larvae also require minimal protection from contamination and do not require a culture medium in order to grow.
The fly larvae are Proteinea’s production platform for five protein engineering projects with pharmaceutical and academic partners. These projects focus on the production of antibodies and vaccines for human and animal use.
“Starting with an antibody, we use our proprietary deep learning model as a very inexpensive and fast method to find better candidates in silico before we do laboratory tests and manufacture the final candidates in our larval system,” says Eljendy.
The company expects its first product, an animal vaccine, to enter clinical trials in the near future.
