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Parth Ajmera

The bioprocess engineering problems in pharma have moved past monoclonal antibodies

#bioprocess#pharma#career

The grad school tracker I'm building is scoped narrowly to protein production cost, but almost every program on it sits inside a much wider field: bioprocess engineering applied to pharmaceutical and life-sciences manufacturing. Before I screen labs any further, I wanted to understand that field on its own terms — how big it actually is, what products run through it, and where the open first-principles problems are, rather than assuming monoclonal antibodies and vaccines are still the interesting part.

How big this actually is

Market-research estimates for "bioprocessing" vary by roughly 2x depending on what a given firm counts as in scope, so I'm treating these as order-of-magnitude, not precise. Fortune Business Insights puts the bioprocess technology market at around USD 51.4 billion in 2026, growing at a 15.6% CAGR through 2034. Grand View Research scopes the broader large- and small-scale biopharmaceutical manufacturing market at around USD 104 billion in 2026, growing at roughly 11.5% a year toward 2035. Continuous bioprocessing specifically — the subset replacing batch-and-hold steps with an inline process — is smaller, around USD 7.3 billion in 2026, but growing at a similar clip. Whatever the exact number, the direction and the driver are consistent across every source: growth is coming from biologics — monoclonal antibodies, vaccines, and cell and gene therapies — not small-molecule drugs, which is where bioprocess engineering as a discipline actually lives.

What products actually require this science

Bioprocess engineering isn't one product category, it's the manufacturing layer underneath several very different ones:

  • Monoclonal antibodies (mAbs) — grown in mammalian cell culture (mostly CHO cells) in stirred-tank bioreactors, still the largest biologics category by revenue and volume.
  • Vaccines — both the older recombinant/inactivated kind and the newer mRNA-lipid nanoparticle (LNP) kind, which is a genuinely different manufacturing process, not just a new molecule in an old process.
  • Cell and gene therapies (CGT) — CAR-T cell therapies and viral vector-based gene therapies (AAV, lentivirus), where the "drug" is a living or biologically-derived product manufactured per patient or per batch, not a chemical made at scale.
  • Recombinant proteins and enzymes — insulin, clotting factors, growth hormone: the oldest and most mature category, closest in spirit to my whey-protein starting point, just with a regulatory bar several orders of magnitude higher.
  • Biosimilars — patent-expired biologics remanufactured cheaper, which is where India specifically competes.
  • Antibody-drug conjugates (ADCs) — a mAb produced by bioprocessing, then chemically conjugated to a cytotoxic payload, so the field sits at the join between bioprocess and synthetic chemistry.

Where the growth and the open problems actually are

The mAb platform is the mature end of this field. Titers went from roughly 1 g/L to double-digit g/L over about 25 years of cell-line and media engineering — a genuine engineering success story, but also why it's not where the interesting unsolved problems are anymore. Three areas are, and they're where I'm now pointing the lab search:

Viral vector manufacturing for cell and gene therapy. This is the sharpest bottleneck in the field. The vector manufacturing market itself is growing fast — one industry estimate puts it at USD 3.55 billion in 2026 growing toward USD 9 billion by 2031 — but reporting on the manufacturing side is blunt about why: viral vectors are produced by living cells, have to assemble correctly, and then have to be purified without being destroyed, and today's affinity resins mostly can't tell a full capsid carrying genetic payload apart from an empty one. Autologous CAR-T, where each batch treats one patient, reportedly costs USD 250,000 to USD 450,000 per infusion to manufacture — there's no mAb-style learning curve yet because there's no single process serving many patients to improve.

mRNA-LNP manufacturing physics. In the lab, LNPs form in microfluidic chips where the flow is strictly laminar. At manufacturing scale, that technology usually gets swapped for impingement-jet or T-mixers running in turbulent flow, and that swap alone changes particle size, encapsulation efficiency, and morphology enough to force teams to re-optimize formulations they'd already finalized. That's a fluid mechanics and mixing problem before it's a formulation problem, which is closer to what I actually studied than anything else on this list.

Biosimilars manufacturing in India specifically. This is the one with a direct path back to my own return-to-India framing. Grand View Research puts India's biosimilars market at roughly USD 184 million in 2026, heading toward USD 1 billion by 2035 at around 21% CAGR, driven by biologics patent expiries and government incentive schemes — reportedly including a new ₹10,000 crore Biopharma SHAKTI initiative announced in the 2026–27 union budget to build out India's biologics manufacturing and regulatory capacity. This is the version of the field where "cheaper protein production in India," the question I started with, maps most literally.

The core challenge underneath all three

Strip the product away and the same first-principles problem shows up in every one of these: what works at bench scale, in a small, well-mixed, laminar or near-homogeneous system, does not scale linearly to a manufacturing vessel or mixer where flow regimes, gradients, and shear all change. A shake flask doesn't have oxygen or pH gradients; a 2,000-liter bioreactor does. A microfluidic chip is laminar; a production-scale jet mixer is turbulent. That gap — scale-down models that predict manufacturing behavior instead of getting rebuilt from scratch at every scale — is the actual research problem sitting underneath vector yield, LNP encapsulation efficiency, and biosimilar cost of goods alike. It's also the reason my own background in fluid mechanics and mixing is more directly relevant here than I'd assumed when I was only looking at this through the whey lens.

That's specific enough to be worth its own list rather than folding into the protein-production tracker: I'm keeping it as a separate pharma bioprocess tracker, screening for labs specifically working on vector purification, LNP mixing scale-up, or scale-down modeling for continuous downstream processing — not just "works on bioprocessing."