TCRs are far from 'engineered' in any real sense, highly dependent on patient MHC and the specific neo-antigen, with no one able to rationally design from scratch a TCR.

Synbio's challenge is not necessarily engineering a genome faster. It's that no one knows what to make, per se. What should we print? We don't know how to design proteins or enzymes. Designing assays to measure a good vs bad design is often the rate-limiting step.

Your first paragraph glosses over all the work that goes into making a useful antibody candidate, a process of iterative modification to meet certain specifications. I don’t see a problem calling this engineering. Your point about market penetration as a judge of ‘engineeringness’ is also silly. There are antibody drug conjugates that serve a specific patient population very well, the fact they aren’t in your ‘top 10’ is irrelevant.

So what if CAR-T cells are struggling in the clinic? It is an entirely new treatment modality. There are people walking around who would be dead without it. You deride it because it isn’t an instant global panacea?

Your point about Synbio is also wrong. Leaders in the field just wrote a position piece in Science and genome writing was listed as one of the major challenges. You’re trying to tell me that George Church ‘doesn’t know what to build?’ Pure bunk.

Try to think of my argument as trying to add higher resolution to the debate, instead of being against/for your argument.

My main argument is against over-crediting what the software and algorithmic component of this is, precisely because I think it is too important. Another wider argument is are humans rationally designing anything / do we understand the wider design principles.

So let's go back to immunotherapy. Neither the target PD1/PDL1, nor the therapies themselves were enabled in particular by "in-silico" methods. Nivolumab and Pembrolizumab were not designed with much algorithmic or software input, but discovered through good old experiments / lab science/engineering. There is no human designer or algorithm in the world where if I give a target, they can rationally design an antibody. What we do is brute force + affinity selection / "directed evolution" to force nature come up with an answer still. We have no clue how to design it like one would design a bridge.

I think CAR-T is amazing technology - my point there remains: the therapies so far did not at use in-silico design of the Chimeric Antigen Receptor so far (the CAR part).. The low success in the clinic is precisely due to our inability to actually design these things properly. Similarly, neoantigen selection remains a big challenge due to lack of rational principles. A CAR is not too different from a TCR or Antibody, which we don't know how to rationally design.

I believe that genome writing is a major challenge. Again you are widely missing my point: Suppose you had a very cheap, abundant, perfect genome writer. What would we write? George, who was an advisor to my first company and kicked off our Hackathon, would be the first to admit that protein design is a major challenge.

Venter wrote an entire organism - and it was mycoplasma verbatim.

We are only able to copy nature or cajole it at the moment, because fundamentally we don't know how to design biology. It is because we don't really understand it very well, which makes it such an exciting time to be in the field.

None of this means these won't be done, or that teams around the world are not trying to do it this moment. There are amazing labs and companies working on these. What I am saying is more specific: Immunotherapy, CAR-T, or genome writing, in its first generation, as it exists, cannot be recruited as a showcase for computationally led, "in-silico" driven advances in biology.

I do believe this will change shortly.

But the narrative that "biotech is becoming more like software" basically ignores the fact that engineering's impact in biotech is limited by our understanding of biology. The biggest challenge in drug discovery is that most targets arent good, and most disease models and in vitro systems are poorly correlated with clinical outcomes

You can engineer all you want, but if you are engineering a drug to optimally bind a target that isnt actually important for disease, all that engineering is wasted. Big pharma companies thought they could "solve" drug discovery with engineering / efficiency improvements in the early days of high throughput screening, but that only works if your biology is correct

The exciting thing about checkpoint inhibitors for cancer is that their targets are actually really important in cancer's strategy to avoid the immune system. The engineering work required is very impressive as well and built upon decades of innovation in that field, but the big pharma companies had a lot of that engineering expertise already I believe. Once they learned the importance of PD-1 / PD-L1 they could execute development campaigns using their existing infrastructure

That points towards acceleration.

>And that reshaping is a pretty lively process. Witness the news that Roche has had a major unexpected clinical failure with their antibody therapy Tencentriq (atezolizumab) in bladder cancer. This is another antibody targeting the PD-1/PD-L1 system, like Keytrude, Opdivo, etc. (in this case, it’s going after PD-L1 itself), and it was the first one approved against any form of bladder cancer. It’s also been approved for metastatic non-small cell lung cancer, which is a more traditional indication in this area, if by “tradition” we mean the last couple of years. The bladder cancer approval, though, was an accelerated one after just Phase II data, with re-evaluation to come after the Phase III numbers came in.

>Well, now they’re in, and the drug missed its primary endpoint. This not only puts Tencentriq’s continued approval for this indication in doubt, but it cannot be good news for the other companies in this space who are targeting it as well. Opdivo (nivolumab) from Bristol-Myers Squibb got approval in February, Pfizer and Merck KGaA’s Bavencio (avelumab) got a similar accelerated approval just a couple of days ago. Merck’s Keytruda (pembrolizumab), meanwhile, showed a good response Phase III (so much so that the trial was cut short), and is under review at the FDA, and there’s AstraZeneca’s entry in this area Imfinzi (durvalumab), too.

https://blogs.sciencemag.org/pipeline/archives/2015/11/17/ap...

>But a similarly targeted drug from Clovis Biotechnology, rociletinib (which is one of the recent acrylamide-containing irreversibly covalent kinase inhibitors), ran into some big trouble. The FDA wants more data, for one thing, and the reason that they want more data is that Clovis submitted preliminary clinical data to them earlier that have not held up. That was clearly done because they were in a race with AstraZeneca (and with time in general), but you’re walking on – or sprinting across – a flaming tightrope when you try something like that, and the results are clear. Clovis’ stock fell about 75% on the news

https://blogs.sciencemag.org/pipeline/archives/2018/02/02/a-...

>A closer look at the data, though, tells an even more different story. That overall POS figure is heavily dragged down by low success rates in oncology. Of the 41040 total pathways in the set, 17368 are for oncology (note that the same drug tried against two different types of cancer will show as two different pathways). The POS of everything outside of oncology is 20.9%, which the POS in oncology itself is 3.4%. If you look at lead indications, instead of all indications, the POS goes up overall (which is in line with earlier studies). But the Phase 2 to Phase 3 transition rate actually goes down a bit, interestingly. Oncology is still the lowest of bunch.

https://blogs.sciencemag.org/pipeline/archives/2017/01/23/i-...

>The timing of this report from the FDA is surely no accident, but it’s always a good time to think about this: the great majority of all drugs that enter clinical trials fail. They fail because they don’t do anyone any good, or because what good they might do is outweighed by some serious and unexpected harm. Around 90% of all compounds that start in the clinic never make it out. Even by the time you get to Phase III – and these are drugs that have apparently already worked in sick patients by that point – the failure rate is still nearly 40%. Drug projects fail constantly.

>It’s hard, sometimes, for people who’ve worked in other industries to appreciate this. Drug development is a unique combination of very high regulatory burden and very high failure rates, so it’s temping to say that the regulations cause the failures. But that isn’t true. Biology causes the high failure rates – specifically, our lack of understanding of biology.

Calling a field where, after spending tens of millions of dollars of development on a product, only 3.4% of them even work at all a branch of "engineering" is perhaps a novel perspective. If 19 out of 20 bridges promptly collapsed after being constructed, the word "engineering" might have different connotations.

As usual in biotech, when I see someone posting simple, obvious untruths for personal gain, I wonder: delusion, or mendacity? Fast DNA synthesis will not solve the actual bottleneck, which is, and has been for a century, clinical trials in humans.

I don’t work in biotech, and also don’t take kindly to being called delusional or a liar. The actual bottleneck isn’t human trials it’s better drugs, and even if trials were the bottleneck, why have you decided that bioengineering needs to be able to design a drug with guaranteed > X% chance of working before it gets to be called engineering? Also, you are ignoring all the other industrial, environmental and agricultural applications of synbio.