Think Forward.

TechBio x Africa Manifesto: The Playbook for Cracking the Translation Bottleneck

by Kamil Seg
20108
Chapters: 3 8.6 min read
This series brakes down the evolution of new breed of companies in the pharmaceutical industry, all AI native. The thesis is simple, biology is immensely complex, more than human cognition can understand from narrow experimental data. It is from leveraging multiple layers, at scale that we can capture the physics behind and develop innovation that translate to life saving drugs. This evolution is now pointing to Africa as continent where next breakthrough can come from. This serie will give you the fundamentals, understand the business model and walk you through the potential for Africa to be the birth place of a new class of techbios

1: Techbios x Africa : The manifesto part 1 6523

Kamil Seg, 4 mon.
- Technology - Science -
2.7 min read.
Closer to Humans: The Next Big Opportunity in TechBio: Hitting Eroom's law in translating assets to clinics If Moore's law promises exponential gains from technology, Eroom's law (Moore spelled backwards) reminds us that drug discovery has stubbornly resisted that curve. For decades, the cost of bringing a new drug to market has roughly doubled every nine years, even as compute and data scaled exponentially. AI-driven TechBios were supposed to break this trend and accelerate discovery, lower costs, and flood the pipeline with new medicines. In its early day's, Recursion was going with something like a 100 drugs in 10 years. And to some extent, they have delivered. Programs from Insilico or Recursion show how AI can compress preclinical timelines from five years down to 18–30 months. Costs are lower, throughput is higher, and in silico tools have expanded the space of molecules Pharma can explore.  But reality is that most AI-first drugs are still aimed at well-known targets, and once they reach the clinic, they face the same bottlenecks as traditionally developed drugs. Phase II proof-of-concept success rates hover at ~40%, unchanged. Back to Eroom's law in action, the bottleneck has shifted downstream. The graph from speed invest tells the story nicely. Early discovery (target validation, compound screening, lead optimization) accounts for ~25% of costs, the bulk of time and money is lost in Phase II and Phase III, where failure rates spike and costs per molecule can exceed 20–25% of the total. Functional Data Is the Missing Piece Why? Because our translational models are still inadequate proxies for human biology. Drugs fail not because they weren't optimized enough in silico, but because they don't behave as expected in humans, showing weak efficacy, unexpected toxicity, or adverse effects that outweigh benefits.  Conversely regulators are now pushing for more personalized approaches: genotyping, deeper disease phenotyping, and companion biomarkers to better stratify patients. That means the next opportunity isn't about yet another molecule generator. It's about building the translation layer: generating functional, human-relevant data at scale.  Two pillars stand out: Bench side. New experimental systems like organoids and organ-on-a-chip can capture human biology more faithfully than animal models, giving us early readouts of drug response in tissue that resembles real patients. it can be high-dimensional functional data (cells content imaging) Bedside. Richer molecular profiling of patients to capture complete responses to interventions across all biological layers. The omics data, reflects physiological responses from the gene expressed to the protein inhibited till the end metabolite produced. This is the frontier TechBios have yet to tackle.  Proprietary datasets from in vitro, in vivo, or in silico work aren't enough, because by design they remain at a distance from real human complexity.  Reminder, the demand is still there as the patent cliffs of 2030 are not going anywhere. The Funding Gap: Bench Traction, Bedside Wide Open The common denominator in TechBio is always the same: proprietary datasets. On the bench side, we're already seeing how this can play out. Just last month, Parallel Bio raised $21 million to push forward a new model for immune drug discovery. Their platform combines organoids and AI is set to generate massive proprietary datasets of immune responses. This 'Immune system in a dish' allows simulate how drugs behave across populations and verify candidates in vitro before they ever enter the clinic. The company dates back to 2021, but recent series A show their gearing up for growth and points to serious answers to the translation problems from Capital Interest. The story on the bedside is very different. Here, the prerequisite is well-characterized patient data of omics like genomics, proteomics, metabolomics, deep clinical phenotyping. Not really the type of data you can engineer in your lab with enough wetware and hardware. Pharma companies guard their clinical trials data as part of their asset. Biobanks have the scale needed but primarily share it with research partners and academics or monetizes them directly, selling access to screened samples and metadata at high prices. Their funded by goverments and charitable organizations around projects with defined partners within a consortio that have their for privilege access.  Hospitals typically generate only small, fragmented cohorts a few hundred patients, often disease-specific and far from the scale needed to train robust models.  And once TechBios push into later stages like preclinical or Phase I, costs spike: recruiting patients, managing trial sites, and running protocols and more tailored to big Pharma economics.  In the West, shrinking patient pools for many chronic diseases add yet another barrier driving the cost further up. This imbalance explains why most visible TechBio innovation so far has come from the bench. Benchside players like Parallel Bio are proving you can generate your own data and own the feedback loop.  On the bedside, by contrast, barriers remain high and that leaves the space wide open.  The real question is not if bedside innovation will emerge, but where. And it may well be that the answer lies outside the traditional Pharma hubs
medium.com/@kamil.seg/feea16383b...

2: TechBio x Africa Manifesto: The Edge - part2 6679

Kamil Seg, 4 mon.
- Technology - Science -
2.7 min read.
In Africa The Bottleneck Was Always Here And Now There a Real drivers for change Translation is now recognized as the great bottleneck of drug discovery worldwide. But in Africa, it has always been the bottleneck.  Not in developing drugs, but in applying them.  Most medicines were discovered and validated elsewhere, then imported with little understanding of how African populations would metabolize or respond to them. The result is a structural mismatch: Africa accounts for 18% of the global population and 20% of the disease burden, yet fewer than 3% of clinical trials take place on the continent, most of them concentrated in South Africa and Egypt. This gap is not trivial. Drug absorption, distribution, metabolism, and excretion (the ADME framework) are heavily influenced by genetic variants, especially in liver enzymes like CYP-450, which remain poorly characterized in African populations.  In theory, Africa's extraordinary genetic diversity should have been a global advantage for understanding variability in drug safety and efficacy. In practice, it was ignored.  As Professor Kelly Chibale of the University of Cape Town has argued:  "If you really want to have confidence in a clinical trial, it must start in Africa. Why? If it works in Africa, there's a good chance it'll work somewhere else, because there is such huge genetic diversity." Then came COVID-19. The pandemic was a turning point, mobilizing governmental, NGO, and international funding to build sequencing labs, train scientists, and set up data infrastructure.  In my opinion, the Africa Pathogen Genomics Initiative (Africa PGI) became emblematic of this shift.  The first 10,000 SARS-CoV-2 genomes from Africa took 375 days; the next 10,000 just 87 days; the following 10,000 only 24 days. Today, all 54 African countries have sequencing capacity, and African scientists identified two of the world's five variants of concern.  For the first time, Africa showed it could operate at global pace when given the tools. These investments were catalytic and revealed what had long been latent:  Africa is not just a recipient of medicines but a potential engine of translational science.  The infrastructure layer, built with public and philanthropic support (like the Bill and Melinda Gates Foundation), is now enabling a broader ecosystem: regulatory frameworks like the Africa CDC and the African Medicines Agency, scientific hubs such as H3D in Cape Town, and new hardware capacity supported by corporates like Thermo Fisher's Centre for Innovative Research in South Africa. From here, the snowball is rolling. What began with genomics is already extending across the translational stack. In Ghana, new medicinal chemistry capacity has positioned the country as only the second on the continent (after South Africa) able to run early-stage compound design, linked into the pan-African Drug Discovery Accelerator. This is big, because the continent can now de-risk potential assets. Pharma is of course watching closely. Roche's African Genomics Program is sequencing tens of thousands of African genomes through local biobanks. Sanofi's partnership with DNDi shows how compounds de-risked in Africa can enter global pipelines.  And demographics strengthen the logic: Africa's population is set to nearly double by 2050, while non-communicable diseases like diabetes, cardiovascular disease, and cancer will become leading causes of death by 2030 which is the same conditions driving Pharma pipelines worldwide. The Continent Is Full Of Bright Tech Minds But data infrastructure alone is not enough; translation also depends on whether there is talent capable of making sense of the data.  COVID revealed this too: it was an African-born (Tunisia) AI company, InstaDeep, that helped BioNTech build the Early Warning System able to flag >90% of WHO-designated SARS-CoV-2 variants an average of two months before their official classification.  The company had already been working with BioNTech on personalized cancer vaccines, and post-acquisition it continues to run as an independent AI lab powering BioNTech's drug discovery, improving AlphaFold-like protein folding in immunology to designing next-generation mRNA cancer vaccines.  The $700 million acquisition in 2023 was not only the largest AI deal outside the U.S. at the time, but also a watershed moment for the continent. As co-founder Karim Beguir put it in a recent podcast interview:  "our initial motive was to prove that young Tunisians, young Africans could innovate and compete at the highest level" The significance goes beyond one company.  It validated Africa's AI talent density, which is being built from the ground up through grassroots, community-led efforts. Initiatives like Masakhane, a volunteer-driven movement advancing natural language processing for African languages, or Deep Learning Indaba, cited globally as a model for how to mobilize a continent around machine learning, are emblematic of this bottom-up energy.  I saw it myself at Applied Machine Learning Days Africa 2024 in Nairobi, where more than 3,000 participants gathered across three days mostly researchers, innovators, and students taking responsibility for local problems and showing how AI can answer them.  This effort-led culture is now being matched with hardware too infrastructure. Microsoft has launched its first Azure cloud region in South Africa, enabling GPU-grade compute to stay on the continent, while Nvidia and Cassava are building an AI factory in Johannesburg, with expansions planned for Kenya, Egypt, Morocco, and Nigeria.
medium.com/@kamil.seg/feea16383b...

3: Techbio x Africa: Early Movers - part3 6906

Kamil Seg, 4 mon.
- Technology - Science -
3.2 min read.
Early Signs, Real Ventures It's one thing to say the infrastructure and talent are here but the real test is whether it yields actual companies.  And the signs are already showing. A new class of TechBios is taking shape, raising money, and doing the first thing every good TechBio does: … drum roll, you should know it by now…  Building proprietary datasets. The support system is forming too. OneBio, a Cape Town venture studio, closed a $47M Series A to back founders at the biology–technology edge.  Villgro Africa in Nairobi has already incubated 40+ health and life science startups and unlocked $18M in follow-on capital. These are strides stimulate the Techbio ecosystem and in part, to close Africa's translation gap with venture tools. And the startups coming out of this wave are telling. I thought I would share my personal pick here. Start-ups I can map on the playbook trajectory. Yemaachi Biotech in Ghana raised $3M from YC, Tencent, and LoftyInc to build the world's most diverse cancer knowledge base, sequencing samples across the continent to power precision oncology. As founder Yaw Bediako put it:  "We're looking at trying to understand cancer in the African diaspora - African American, Black British, and continental Africans - the first initiative of its scale. You can't say you're studying a disease if you don't include the most diverse population on the planet, which is the Black population."  BioCertica in South Africa, backed by Pronexus and the Gates Foundation's I3 program with a $2.2M seed, runs consumer genetic tests but is really playing the long game of building the first African polygenic risk database.  And Bixbio, part of OneBio's portfolio and an Illumina Accelerator graduate, assembled the largest reference dataset ever from Southern Africa, nearly 400 high-quality genomes across eight ethno-linguistic groups. Even newcomers like Pandora Biosciences are starting on the same path, building chronic disease datasets designed for drug discovery. And just this summer, the signal got even stronger. In June 2025, Revna Biosciences, a Ghanaian precision medicine startup, announced a landmark partnership with AstraZeneca. Within months, EGFR (gene coding for cell growth protein) biomarker testing integrated into Ghanaian cancer centers, oncologists trained in precision protocols, and the rollout of one of AstraZeneca's targeted therapies for lung cancer patients.  For a sub-Saharan market that has historically had near-zero access to this kind of precision oncology, that's nothing short of historic.  As Revna's CEO Dr. Derrick Edem Akpalu put it:  "This collaboration exemplifies how a synergized biomedical ecosystem such as RevnaBio's can help address long-standing institutional voids that have limited access to advanced molecular diagnostics and targeted therapies in this region."  It's a textbook case of a TechBio going from data and diagnostics to being a direct bridge for global Pharma into Africa. None of this is random. Data-first plays are the starting point of TechBio always.  In the West, consumer genomics followed the same arc: 23andMe built a database of 15M genomes, went bankrupt, and still got snapped up in 2025 by Regeneron for $256M because Pharma wanted the dataset.  Tempus, sitting on 20 petabytes of oncology data, signed a $160M licensing deal with Recursion to train AI models for biomarker discovery and patient stratification.  The lesson is obvious: even before a molecule is in sight, the data itself is valuable enough to Pharma. Africa's first TechBios are now running that playbook and they're doing it from the most diverse human dataset on the planet. The Stakes for Africa x TechBio Case Study: 54gene - The Right Start, The Wrong Turn 54gene was supposed to be Africa’s genomics moonshot. Founded in 2019 by Dr. Abasi Ene-Obong, the company set out to fix the glaring gap where less than 3% of global genomic data came from Africans despite the continent holding the greatest genetic diversity on earth. Backed by Y Combinator, Adjuvant Capital, and Cathay AfricInvest, it raised $45M across three rounds and quickly became the poster child for African TechBio. The model at first was exactly what you’d expect from a good TechBio: start with the data. 54gene partnered with 10 of Nigeria’s largest hospitals, built a biobank that grew past 100,000 patient samples, and focused on high-value cohorts like cancer, cardiovascular disease, diabetes, and sickle cell. This was the right first play: position as an enabler for hospitals and research centers, pile up proprietary datasets, and generate revenue through paid Pharma collaborations. In other words, service-led first, platform-led later — the same arc followed by U.S. genomics pioneers like 23andMe. Then came COVID. 54gene pivoted into diagnostics, scaling mobile labs and at one point driving Nigeria’s daily testing capacity from 100 to over 1,000. Revenues spiked — over $20M from COVID testing — but the pivot also pulled the company away from its core playbook. Instead of doubling down on turning its biobank into translational insights with AI, it spun up Seven Rivers Labs, a costly diagnostics arm. The bets didn’t pay off. By 2022, as COVID demand collapsed, 54gene was caught between a fading diagnostics business and a stalled genomics mission. Layoffs, valuation cuts, and boardroom fights followed. In 2023 the company shut down operations; by 2025, its assets, including the biobank of 100,000 Nigerian genomes were up for sale at just $3M, before a Lagos court froze the deal amid lawsuits between founder and investors. The story matters because it shows how fragile the trajectory can be. Imagine if instead of diagnostics, 54gene had invested its datasets into AI models to map dosage differences for African populations, identify new drug targets, or partner on stratified clinical trials. That’s the road from platform to assets, the road that makes a TechBio a unicorn. Dr. Ene-Obong seems to agree. His new company, Syndicate Bio, is now doubling down on the same thesis but with AI built in from day one partnering on cancer genomics in Nigeria and aiming to turn Africa’s diversity into global drug discovery. It’s the continuation of the playbook 54gene set in motion, but with the missing piece restored.