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How to excel at a startup

How to excel at a startup

9 actionable tips for startup employees & a Deep Dive on Drug Discovery & Development

Giammarco Pacifico
September 06, 2023

Getting into a startup is the easy part.

Excelling at a startup is a completely different thing.

Some things you can learn from books. Others you can only learn through experience and with the battle scars to prove it.

I have spent the past decade in the startup world and this is what I’ve learned about being exceptional at building a startup.

Buckle up for some controversial topics.

1. Dealing with problems you did not create

In a startup, you’ll face many situations that were created outside of your control. And most of them will suck.

You did not do anything to end up there but because it impacts the company, it’s your problem and it’s your job to solve it.

If you don’t do it (or find excuses about it), it will stop your company from moving forward or it will get you fired.

Here are a few examples:

Your supplier went out of business after closing a large order.
The company lost money (good luck with being on the creditors list) and you need to find a way to get the parts before the next prototype delivery in a few weeks
Market changes and investors who promised more money for reaching specific milestones change their minds.
After hitting those milestones you learn that the goalpost has changed. It does not matter why it did, it’s your problem to go and fix it because the company depends on it
The big company that gave you a letter of intent pulled back.
You felt like it was the perfect partnership but leadership changed and the new strategy does not include working with you anymore. You leveraged this partnership during your last fundraise and now you are out of luck with your pipeline

❝

It’s Not My Fault, But It’s My Problem

Someone should put this on a T-shirt

2. Ask for forgiveness, not for permission

This one is probably a given but repetita iuvant.

If you’ve joined an early-stage startup and you need the CEO's approval for everything, you have a big problem.

The expectation is that you independently go ahead with your projects and decide when it’s critical to align with your team versus sprint ahead.

3. Overcommunication wins

You might have worked in places where you’ve been told that it’s best to only share updates on the projects that are most relevant for the group.

This doesn’t work in an early-stage startup.

As opportunities are born (and die) continuously you don’t know beforehand what is relevant and what is not. The only way to jump on a potential game-changer or avoid a bullet coming your way is to know very well everything that your colleagues are working on.

4. Tolerance for ambiguity

Conflicting or ambiguous situations are more common than you can ever imagine in any startup.

There will be times when you might need to make the most of a “grey area” or bluff your way out of a tricky situation without stressing out.

Other times you need to make a decision holding 2 conflicting points of view in your mind, which both have their merits but you can’t pick one side.

❝

The test of first-rate intelligence is the ability to hold two opposed ideas in mind at the same time and still retain the ability to function.

F. Scott Fitzgerald

5. Learn how to prioritize

At any time, you’ll have on your plate more things than you can handle.

This is normal and you should never expect to achieve an empty to-do list at the end of the day.

It’s of vital importance that you decide what should be prioritized at any given moment. You’ll always struggle between things that are urgent and things that are important. Make sure you set time aside for both.

Prioritizing tasks happens on multiple levels.

On one hand, you might be inclined to start with projects where you feel more confident because it’s something you’ve done before.

This is rarely the right way to go about it.

In a small startup, knowledge (even superficial) is rare and you might not be good at financial modelling but you could be the best in your team.

Therefore, it’s important that you prioritize financial modelling instead of writing a grant proposal because there are other people who can do that too (even if not as well).

Another aspect is discerning between the tasks that came to you and the ones that should be on your plate but are not there yet.

Anticipating projects that have to be started now so that the company can achieve milestone X in a few months is as important as dealing with what your boss tells you to do.

So ask yourself the question: “What is not on my to-do list that should be?”

In short, prioritization happens on the relative scale of the people you work with and based on what is not there on the list.

6. Blitz emotional intelligence

Emotional intelligence is arguably important in every aspect of life and work.

In a startup, you need its blitz version (shoutout to all the chess lovers), where you won’t have a brief to prepare for a meeting in advance or hours to put together the perfect presentation.

You’ll need to continuously read the room and change your approach based on it. This is true both internally (especially with the leadership/board) and externally (with investors, partners and clients).

7. Over-promise and over-deliver

You probably learned that the best way to excel in your career is to underpromise and over-deliver.

This does not work in a startup.

Over-promising is how the company got off the ground in the first place, and how it got to the point where it is right now.

Over-delivering is the only way to guarantee that it will stay the course.

8. Busyness ≠ growth

Make the most of your time by scheduling your tasks based on the type of brainpower required. Some things require a deep focus on uninterrupted work whereas others require you to get manual things off your plate.

Being tired of one does not mean not being able to do the other ones.

There is countless productivity advice. Here are some of my favourite tips:

But please don’t turn into a productivity maniac for the sake of it 🙏

9. Moving fast breaks things and people

Everybody knows that in a startup you have to move fast.

But because there is no free lunch, this means sharing your work and delivering projects before you’re very proud of them.

You won’t have the luxury of spending a lot of time refining things before sharing them because you’ll be wrong most of the time. And wasting countless hours refining something that is going to be useless will kill the company.

Instead, sharing an ugly first draft allows everyone to evaluate the direction and align on what needs to be done before the final version.

This applies to everyone, from the intern to the CEO. Nobody has the certainty that the initial idea is the right one and therefore it’s never worth optimizing for details before getting initial feedback.

Think of it like writing a book: the best writers are great editors who are not stopped by a black page. Instead, they start by putting out everything they’ve got in a messy way without judging themselves too much. Only after, they go back multiple times to refine the draft into better versions.

The less spoken aspect of this is that you also won’t have time to align with everyone in your team and include everyone’s feedback before

Enjoy killing it in your next startup role!

Drug Discovery & Drug Development - The basics

Today’s deep dive was going to be about a new way of launching biotech companies.

But then I realized that we should first cover the current way of doing that.

As a bioengineer, I had to learn the biotech drug discovery world the hard way, by getting my hands dirty on the job while trying to get as much knowledge from people around me.

If you are also not a trained chemist or biologist, I hope this helps you clarify some concepts and if you are already an expert, repetita iuvant (twice in the same issue…talking about repetition).

Drug discovery and drug development are sometimes interchanged, which increases the confusion for non-experts but on a high-level view, there are 4 stages of discovery and pre-clinical development. After that, a drug enters clinical trials for human testing in multiple phases before approval and market entry.

Image source: Synthego

Target identification

Drug targets are the biological entities that a drug can interact with to produce a specific effect of stopping harmful mechanisms or promoting positive ones.

What’s a target?

A target can be a protein, an enzyme, DNA, RNA, a protein complex or any molecule that plays an important role in a certain condition.

What is a good target?

Target that is present in the human body
This sounds obvious but many diseases are caused by a gene deletion where the lack of a protein expression leads to a condition. In these cases, it’s not possible to drug the target directly and scientists have to use other indirect mechanisms, often against other targets
Target and disease should have a causal relation
Correlation is relatively easy to prove in biology as humans are complex interconnected machines. Causation is much more stringent and it may take years to rigorously prove

The most common way to approach target identification is to start with a known disease and then search for proteins that participate in the observed symptoms.

But how can researchers identify which proteins are misregulated or which genes are not properly expressed?

This is where disease models play a fundamental role.

In the case of cancer therapeutics, for example, a disease model can be built from large databases of patients' genomics data. By studying and aggregating mutations and downstream effects of multiple cohorts, it’s possible to infer patterns and abnormalities when compared with healthy patients.

This is where traditional in-vitro techniques can be associated (and potentially replaced) with computational models, starting with AlphaFold, which is a well-known open-source protein prediction software.

But this a topic for another deep dive.

Target validation

The first step is to assess how easy it is to hit the identified target. Targets that are located inside a membrane are impossible to reach with certain therapies and the ones inside the nucleus (for example DNA-related) are even harder to get to.

This will determine the different approaches and mechanisms that will be tried to reach the target.

In addition to the delivery method, it’s important that hitting a target does not cause any other downstream issues or does not hit any non-targets. Hitting critical pathways in our body could help with the diseases but almost certainly impact other systems. On the other hand, a drug could have an effect (even partial) on multiple targets in our body, some of which might be off limits for their potential side effects.

These are called “off-target” effects.

As you have realized by now, target validation is about laying the foundation for drug efficacy, and given that 44% of Phase II clinical trials fail because of lack of efficacy, this is a critical step.

Proving the efficacy of modulating the identified therapeutic target can be achieved in a few different ways. For example:

Genetic mutations in humans
(gene-disease association studies based on public/private databases as well as empirical evidence)
Gene knock-out studies in mice
(one or more genes are made inoperative and the downstream consequences are studied compared to normal mice. To limit systemic outcomes that could interfere with other systems, conditional tissue-specific and time-specific gene knock-outs have been developed)
small interfering RNAs - siRNAs
(used to interfere with the expression of genes that have complementary nucleotide sequences, which effectively means knocking down the gene of interest)

Hit identification

Beginning this step is a cornerstone of the drug discovery and development process because it indicates the commitment to pursuing a specific target by identifying the set of molecules that bind to that target.

Where are these hits coming from?

Known molecule from the published literature or patents
Random screen of a large compound selection (traditionally done using High Throughput Screening or HTS)
Direct screen of a smaller group of compounds pre-selected based on prior knowledge of the chemical class or target
Fragment screen of a few thousand compounds at low molecular weight, screened at high concentration
Virtual Screen, in-silico analysis of large libraries. Can be ligand-based (starting with the ligand that binds the target) or structure-based (starting with the protein structure and evaluating potential ligands)
DNA encoded library, screening large collections of molecules all at the same time by tagging each molecule during DNA synthesis

Image source: Sygnature Discovery

Lead identification (or hit to lead)

This is the process of selecting which hit to focus on going forward.

The first step is to individually confirm the hits obtained from the previous step with different methods, such as the following:

Confirmatory testing: using the same conditions used during the screening phase
Dose-response curve: test over a concentration range to assess the half-max binding concentration
Secondary screening: test in a functional cellular assay for efficacy
Synthetic analysis: evaluation of synthesis feasibility, up-scaling and cost of goods to manufacture
Biophysical testing: assessment of whether the hit effectively binds the target focusing on the kinetic and thermodynamic components. Examples: Surface Plasmon Resonance, Nuclear Magnetic Resonance
Freedom to operate evaluation: verification of patentability

What makes a good lead (or lead cluster)?

High affinity towards the target (and low affinity towards off-targets)
Proven efficacy in cellular assays
Druglikeness (qualitative assessment of how easy it is to turn that into a drug, mostly based on solubility in water and fat and molecular weight)
Low toxicity
Metabolic and chemical stability
Synthetic tractability
Patentability

Multiple hits are usually analyzed in groups, which allows the optimization of selected compounds based on their analogues before starting to optimize one specific lead. Usually, 5-10 lead compounds are required in a cluster to confirm options for diversity.

Lead Optimization

This process consists of taking one lead and converting it into a pre-clinical candidate by chemically modifying its structure based on its pharmacokinetics and pharmacology properties.

The most common framework is ADME (absorption, distribution, metabolism, and excretion), which describes the disposition of a pharmaceutical compound within an organism.

This is often the most difficult and challenging part of the whole drug discovery process and it is extremely dependent on the compound. Moreover, optimization methods are often trade secrets tightly held within different biotech and pharma companies.

The best way to become an expert in this part is on the job or by reading case studies or similar/adjacent compounds.

Preclinical development

The primary goal of preclinical studies is to determine the initial dose for a first-in-human study and assess the toxicity of the compound.

This is done through animal testing, which can involve different species (the most common ones are mice, dogs, monkeys and pigs).

The species is chosen based on the best correlation to human trials. For example, canine tests are not the most suited for solid oral dosages because their carnivore’s intestine is underdeveloped compared to human’s omnivore one. On the other hand, mice are rarely chosen for antibodies because of well-known adverse effects on their bacterial flora.

Animal testing is a long process, starting with in-silico animal models that allow scientists to choose not only the most suited species but also which adjustments have to be done to compensate for systemic differences in animals while not losing too much in terms of human translation.

In addition to safety and efficacy, there are many more parameters to consider during animal trials, for example:

The distribution of the compound within different organs (especially the brain and blood) and any long-term residues
Route of excretion, using radiolabeled compound
Effects on the metabolism and potential formation of metabolites
Drug-drug interaction studies if the compound is supposed to be taken together with other drugs based on the indication guidelines

Before human trials authorization, both FDA and EMA usually require testing in 2 mammalian species, including a non-rodent one, following oral and/or intravenous administration.

Based on these pre-clinical trials, no-observed adverse-effect levels (NOAELs) are determined to set up the dosage levels for patients, taking into account uncertainty due to animal models and individual-to-individual differences.

I hope this deep dive, sparked your curiosity to learn more about drug discovery and drug development - if you want to learn more check out.

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Latest funding rounds in health & bio

Ready to turn this news into your next career opportunity? Here is how

Mysthera Therapeutics launched with a $3.5M round to develop first-in-class oral therapeutics to treat complex autoimmune diseases 🇨🇭
Calopad closed 4M CHF for its heat patch that keeps your body at the optimal temperature for muscle regeneration or the treatment of pain 🇨🇭
Enough raised $43M to further develop their plant-based, meatless products based on fermented fungi and scale their B2B model 🇬🇧
Teale, an employee mental health platform that partners with HR functions across organizations, has raised €10m 🇫🇷
clock.bio closed a $4M funding round for their stem cell self-rejuvenation approach to prevent and treat age-related diseases 🇬🇧

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