While the successful therapeutic targeting of transcription factors is a relatively new undertaking by industry, these proteins themselves are nothing new. Transcription factors are well-known entities – they are the hairclip-shaped molecules that when activated, travel into to the nucleus, grab onto DNA, and drive the transcription of genes into proteins.

Moreover, transcription factors have been shown to play a role in a broad range of diseases, ranging from cancer to autoimmune disease, to cardiovascular disease, to neurological disorders. The roles of certain transcription factors in disease pathogenesis have been well-elucidated and scientifically substantiated:

• Robust genetic validation: Through reviewing genome-wide association studies, we are able to unearth correlations between upregulated levels of certain transcription factors across patients with certain diseases.
• Substantial biological validation: As transcription factors play central roles in the up-/down-regulation of distinct signaling cascades, we have the ability to look to other pathway regulators, whether that be upstream or downstream, to understand the degree to which a given pathway’s modulation has on disease.

Given this ever-increasing body of evidence, why is it that for so long, they have not been the focus of drug discovery? The short answer is that it has not been for lack of effort – an understanding of transcription factors themselves, and how they are regulated, helps explain.

Traditional drug discovery is largely “active-site directed” – meaning inhibitors of proteins have historically been designed to latch onto the active, or catalytic, sites of proteins, thereby blocking their ability to undertake further action or function. And herein lies the challenge for transcription factors! Lacking active sites entirely, transcription factors are instead largely regulated by post-translational modification, which in turn impacts changes in conformation, behavior, and interaction with other proteins.

However, in recent years, huge leaps have been made in drug discovery – ranging from novel technologies unearthing new techniques for small molecule development to new modalities like targeted protein degradation – that are beginning to open the door to a promising wave of transcription factor-directed therapeutic candidates.

One area of interest is that of oncology, where growing evidence has shown the role that transcription factors play in driving various cancers. C4 Therapeutics is one company leading the therapeutic application of protein degradation, a process by which target proteins are “tagged” with ubiquitin to cause degradation of the target. C4’s leading program, cemsidomide, is focused on targeting IKZF1/3, transcription factors that drive cancer cell proliferation and survival in multiple myeloma and Non-Hodgkin lymphoma. Other companies, like Vividion Therapeutics and Flare Therapeutics, are applying new approaches to small molecule drug development to unearth previously undetectable druggable pockets that play important roles in transcription factor activation, with clinical-stage programs in distinct cancer settings.

Another promising area of scientific advancement is in the treatment of autoimmune disease, where numerous biotechnology companies are deploying innovative techniques to target transcription factors that are directly implicated in disease. Companies like Kymera Therapeutics and Nurix Therapeutics are applying their respective protein degradation drug discovery platforms to the development of transcription factors including STAT6, a regulator of the IL-4/IL-13 pathway that is implicated in a range of Th2-mediated allergic diseases. Another company pursuing a novel approach to transcription factor targeting is Recludix Pharma, which is leveraging its SH2 domain-directed platform to selectively target STAT6 and STAT3, another transcription factor in the STAT family that is implicated in Th17-driven autoimmune disease.

At HotSpot, we too have homed in on the transcription factor target class as an exciting application for our allosteric drug discovery platform. Our platform allows us to identify and unlock the control mechanisms that exert functional influence on a protein’s activity – a technology ideally suited to the targeting of transcription factors. Through our platform, our early research has enabled dozens of transcription factors implicated across a wide range of disease, from immunology, to oncology, and beyond.

Leading our pipeline is our IRF5 inhibitor program, a transcription factor that functions as a master regulator of innate immunity. IRF5 utilizes a triple-mechanism approach, impacting autoantibody production, interferon levels, and the production of pro-inflammatory cytokines:

IRF5 has been shown to have striking genetic validation in numerous diseases, including systemic lupus erythematosus, Sjögren’s syndrome, rheumatoid arthritis, and other autoimmune disorders. Moreover, each of the signaling pathways regulated by IRF5 have varying degrees of clinical validation, with drugs either approved or producing promising clinical data that play a role in regulating upstream or downstream factors. Given this profound genetic, biologic, and clinical validation, it’s no surprise that an IRF5 inhibitor has been long sought-after by industry – yet the unique challenges presented by IRF5 have led to failure after failure.

At HotSpot, our platform has uniquely enabled the discovery and development of highly potent and selective small molecule inhibitors of IRF5, which are in turn yielding compelling in vivo data proving out the triple-mechanism effects of an IRF5 inhibitor. As we progress our program through pre-clinical development and into the clinic, we aim to marry this activity with a favorable tolerability profile and convenient oral dosing to bring forward a highly differentiated and convenient treatment option for patients.

We look forward to the continued advancement not only of our own IRF5 program, but also to the collective advancement of this novel class of therapies with broad-ranging potential across disease. As this wave of programs progress into and through clinical development, we’ll begin to uncover if these innovative drug discovery technologies are finally able to scratch the surface of the therapeutic promise of targeting the transcription factor class – which, in the long run, has the potential to yield many more waves in the years to come.

The post A New Wave of Therapeutic Innovation Through Targeting Transcription Factors appeared first on HotSpot Therapeutics.

As we continue to evolve our Smart Allostery platform, we recognized a fundamental analogy when scrutinizing these naturally occurring pockets; they exhibit certain characteristics implicative of recurring patterns, akin to repeatable images with special characteristics. The primary challenge, however, resides in the precise identification of these patterns within extensive and intricate deep datasets. To address this challenge, we took lessons from facial recognition, a specific subset of pattern recognition. By deploying principles akin to facial recognition patterns such as eyes, nose, mouth, and overall facial structure to locate the “face” of natural hotspots, we aim to bridge the gap between the visual typology discernible to a seasoned scientist and the potent and high throughput capabilities of machine learning for drug discovery.

Given that the applications and the technologies supporting accelerated pattern recognition are evolving rapidly, we thought it would be helpful to shed light on the significance of pattern recognition in drug discovery and its role in potentially solving complex biological problems. We will explore the essential ingredients necessary for successful pattern recognition, emphasizing the importance of bringing together the right visual data coupled with the right human drug hunter’s insights and the right algorithms.

Cracking the Code for Successful Pattern Recognition

We believe that solving complex problems with data insights and predictions requires the following:

1. A Good Problem: The first ingredient for successful pattern recognition is to define a good problem. This involves identifying a task or challenge that can benefit from pattern recognition techniques. Whether it’s image or speech recognition, fraud detection, or predicting customer behavior, a well-defined problem lays the foundation for effective pattern recognition. Success in drug discovery is critically dependent on selecting the right target biology and how to interrogate that biology. This approach recognizes that proteins have regions beyond their primary functional sites that can be strategically targeted to achieve desired therapeutic outcomes.

For HotSpot, our primary focus is to systematically deliver first and only allosteric small molecules across multiple target classes relevant to oncology and immunology.  A crucial element requires gaining a deeper understanding of the intricate mechanisms that underlie these diseases and their associated biological pathways. We believe that by comprehending the holistic functionality of potential drug targets within these pathways, as well as understanding the pathways’ roles in the context of the disease they affect, will ultimately pave the way for groundbreaking approaches to drug discovery.

• A Good (and Large) Data Set: Data is the lifeblood of pattern recognition: A substantial and diverse data set is essential to extract meaningful patterns. The data set should accurately represent the problem domain, capturing the variations and nuances that need recognition and understanding. The availability of a large and high-quality data set enables the model to learn robust patterns and generalize well to new data. Datasets to address these problems are plentiful, spanning mutations, genomic sequencing, protein structure and small molecules. With that said, the challenges of collecting and cleaning such data should not be underestimated, due to its scattered sources and various formats. Fortunately, the good news is that machine learning is making significant advancements in handling noisy data and extracting valuable information from diverse data types.

We have applied these key learnings and created a comprehensive database inside the SpotFinder component of our platform. It is meticulously curated to incorporate the essential data required for the application of large-scale machine learning and pattern recognition algorithms. The data are gathered from widely used publicly accessible databases, as well as information extracted from scientific literature through advanced natural language processing techniques.

• A Key Insight into the Problem: Insight plays a vital role in pattern recognition. It involves understanding the problem domain, identifying relevant features, and discerning the underlying patterns that lead to accurate predictions or decisions. This insight could come from domain expertise, exploratory data analysis, or prior research. Having a deep understanding of the problem enables the development of effective algorithms that capture the relevant patterns.   This is the point of interaction between machine learning/pattern recognition.  

• The Right Algorithm to Interpret the Data: Selecting the appropriate algorithm is crucial for pattern recognition. Different algorithms, such as decision trees, support vector machines, neural networks, or clustering methods, have varying strengths and limitations. The choice of algorithm depends on the problem at hand, the nature of the data, and the desired outcomes. The selected algorithm should be capable of capturing and interpreting the patterns identified through insights, leading to accurate predictions or classifications.

Post Hoc Thought

Recognizing that drug development is highly complex and multifaceted, pattern recognition can serve as a fundamental and robust approach to solving complex problems. It encompasses the capability of machines to detect patterns within data and subsequently employ these patterns to inform decision-making or predictions through computer algorithms. This function is an indispensable component of modern AI systems.

By focusing on a well-defined problem, a good data set, key insights, and the right algorithm, scientists can unlock the potential of pattern recognition and make significant strides in solving real-world clinical challenges.

The post Drug Discovery: Taking Inspiration from Facial Recognition appeared first on HotSpot Therapeutics.

Advantages of allostery 

The advantages of allosteric molecules are well characterized:  allosteric drugs encounter less competition from endogenous substrates when targeting proteins leading to enhanced potency in living systems; and due to their highly selective nature, allosteric molecules often have an improved therapeutic index. However, when taking a slightly closer look, it becomes apparent that the actual “magic” of allostery lies within Nature’s myriad ways of controlling protein function (see figure below). Utilizing modes of action, allostery allows one to imagine drug molecules with a broader set of pharmacological properties than traditional active site modulators. Whilst active site drugs simply shut down substrate turnover, an allosteric modulator can change a protein’s shape and additionally prevent its localization to a different part of the cell. These traits of allostery provide a new canvass for creating important therapeutic molecules and open up new pathways within the area of highly differentiated pharmacology.

Finding allosteric sites

Despite the promise of allostery, the identification of allosteric binding sites and respective binding partners remains challenging. Therefore, it is exciting that HotSpot Therapeutics is joined by others who are seeking to break open this area of science.

It is well recognized that many allosteric inhibitors are found through phenotypic screening but, even if you are successful in identifying a well-behaved ligand, it is far from guaranteed that you can work out the actual underlying mechanism.

More rational approaches take advantage of structure based data, promoted by HotSpot Therapeutics, Nimbus Therapeutics and Relay Therapeutics, the latter using “motion based hypotheses”, described in the Biocentury article.

To overcome challenges of allosteric drug discovery, at HotSpot we are integrating rational structure-based approaches, proprietary chemistry, and tailored assay formats.

• Structure-based approaches: Our SpotFinder platform identifies regulatory pockets on proteins that can be targeted with small molecules.  We leverage large quantities of structural and sequence information to identify common allosteric pockets within and across target classes, thereby creating annotated ‘pocketomes’ which function like encyclopedias of the identified pockets.  Using cutting edge machine learning approaches, we are uncovering the unique fingerprint of allosteric regulatory pockets.
• Proprietary chemistry: We invest heavily in custom chemistry to drug the identified sites in order to go beyond the active site chemotypes that are typically poorly suited for allosteric sites.  This philosophy is borne out by our colleagues at Vividion Therapeutics who are demonstrating the value of exploring new covalent chemical space in the context of a powerful proteomics platform. Furthermore, like Revolution Medicines, we exclusively focus on targets that are structure enabled, as this rational approach allows us to progress the chemistry more rapidly.
• Tailored assay formats: It is critical to rapidly confirm the predictions of the platform through carefully engineered proteins and cell-based methods. This way, we ensure that our molecules are hunting their targets “in the wild” and not only just in silico.

This combined approach has allowed us to create a platform that is now delivering chemical starting points across a number of sought-after target classes. As shown by the diverse landscape of allosteric companies described in the Biocentury article, it is really an exciting time to be pushing the boundaries of what is possible with chemistry.  In the end, patients are going to benefit from the breadth of targets and diseases that we are going to be able to address.

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Nature commonly uses specific allosteric sites, distal to their active engines, to regulate the functional activity of proteins. Because of this potential powerful role, allostery has fascinated the pharmaceutical industry for decades as an approach that could open up otherwise challenging drug targets.

To date, the industry has made reasonable traction at making allosteric modulators for ion channels and transmembrane proteins on the surface of cells. Positive allosteric modulators (PAMs) and negative allosteric modulators (NAMs) are being exploited to regulate the activity of many cell types, but especially neurons, including the well-known benzodiazepine class of drugs. A broad range of PAMs and NAMs are being explored in clinical development today, including NMDA and SK2 channel programs.

This flavor of allostery has typically been uncovered via phenotypic cell-based screens looking for a downstream cellular effect; in many ways, these allosteric-site engagers are found by experimental “accident” with their binding sites only deconvoluted after the fact. These phenotypic screening methods to find allosteric modulators extends beyond just ion channels; agonists of enzyme activity in lysates and in cells can be uncovered in this HTS-enabled fashion, as occurred with our glucosylcerebrosidase (GCase) activator program at Lysosomal Therapeutics, now in Phase 1 for Parkinson’s Disease.

In light of the role of phenotypic screening in most cases, it’s no surprise then that most of the existing small molecule allosteric drugs were not created as a priori allosteric regulators of a protein, nor were they computationally-powered, protein structure-enabled programs (SBDD).

That said, there are a number of recent and notable examples of allosteric inhibitors discovered with significant input from SBDD, like Bcr-abl, Akt, HIF-2a, and SHP2 (here). At Nimbus, we also exploited this allosteric discovery strategy with both our acetyl CoA-carboxylase (ACC) and Tyk2 inhibition programs.

With ACC, Nimbus chose to go after an allosteric site on a regulatory domain of the enzyme, rather than the catalytic site where most of the industry had broken their pickaxe. Using SBDD, and enabled by a natural product that blocked the site, Nimbus discovered a potent inhibitor with attractive drug-like properties. After exciting early clinical data, Gilead acquired the program and is advancing it in NASH (here).

Building on these successes, Hotspot Therapeutics was founded to systematically approach the allosteric modulation of intracellular proteins by building a platform to exploit nature’s protein regulatory mechanisms. Today we are officially launching HotSpot out of seed-stage stealth mode with the closing of $45M Series A round of financing (http link).

Beginnings: It Starts With the Team

Hotspot was founded by CEO Jonathan Montagu and CSO Gerry Harriman. These two seasoned executives are very well known to Atlas, as they were two of our initial hires when I was acting CEO of Nimbus in 2010. Jonathan was the first CBO of Nimbus and Gerry led the successful discovery and early development of the ACC program. I had the pleasure of working with both of them very closely for a 4-6 years, and they left Nimbus in 2014 and 2016, respectively.

The two of them began to noodle on the initial concept for HotSpot in late 2016, and we committed to seed the effort in December 2016, largely on the basis of two great serial entrepreneurs (who became Atlas EIRs) and a compelling concept paper. Other than our collective experience with SBDD-driven allostery, HotSpot had no specific lead matter around new targets or any new “rules” for allostery discovery at inception. It was a bet on a founding team with a great idea.

Gerry and Jonathan had a clear vision of what they wanted to build: a structurally-enabled product engine platform to a priori identify targets regulated by natural allostery and to discover novel drugs that exploit those mechanisms.

Shortly after seeding the startup in March 2017 with our friends at Sofinnova Partners (whom we had just worked closely with at Delinia), HotSpot assembled its core scientific team: Oscar Moradei drives medicinal chemistry (ex-Epizyme, Merck); Ara Aslanian heads up our in vitro pharmacology efforts (ex-Concert, Novartis); Peter Fekkes brings strong biophysical expertise as he leads the new targets effort (ex-H3, Novartis); Suresh Singh (ex-Vitae, Merck) has led our computational chemistry work; and, most recently, Christian Fritz joined to lead molecular pharmacology and translational biology (ex-Syros, Infinity).

With this solid team as a foundational element of our HotSpot investment thesis, what’s the nature of the opportunity?

Hotspots: privileged, natural allosteric regulatory sites

Drug discovery programs aimed at a protein’s catalytic or orthosteric site often face an uphill challenge in their quest for potent, selective, drug-like molecules. First, the inhibitor has to compete against natural substrate, which can reach very high concentrations inside of cells (e.g., ATP is mM in the cytosol); this creates high potency and target occupancy hurdles. Second, nature has largely conserved the catalytic engines that effect similar processes inside of cells (e.g., phosphorylating proteins via kinases), creating selectivity challenges that manifest as “off-target” concerns. Lastly, many substrates are charged or greasy (hydrophobic) which affects the character of these binding sites, biasing lead discovery towards chemistries with “un-drug-like” properties, challenging prospects for oral bioavailability and adequate pharmacokinetics.

Allosteric sites, in contrast, get around these liabilities by exploiting more natural regulatory contexts. Nature has evolved precise mechanisms for controlling protein function, often taking advantage of post-translational modifications (or PTMs, like phosphorylation, acetylation, methylation, etc). For example, many proteins are known to have large unstructured domains, far from their active sites, that get phosphorylated, inducing changes to protein structure through intramolecular phospho-regulatory binding sites. The same tertiary structure changes can happen with acetylation and other modifications.

The natural pockets where these PTMs fold or bind in unique ways create allosteric sites that are deemed regulatory “hotspots” – hence the name of the startup.

Drugging hotspots has some advantages over catalytic pockets. These sites don’t compete with abundant natural substrate. Because nature needs sensitive on/off mechanisms, these are typically very low affinity interactions. Further, they are often formed by unique protein sequences, which help solve the selectivity problem of conserved catalytic machinery.  These sites also frequently have a more balanced drug-friendly character when it comes to their charge, hydrophobicity, and size. Lastly, many protein targets that are undruggable by conventional inhibitors can be targeted via these regulatory hotspots. All of this makes hotspot drug discovery incredibly compelling.

In addition, these hotspots are often superior to conventional forms of allostery: they are amendable to systematic SBDD approaches rather than phenotypic screens; they by definition have functional relevance vs other “cryptic” or random “computationally identified” protein pockets; and, the natural PTM ligand itself provides a starting point for a structural understanding of the site and initial chemical equity.

As for the scale of the opportunity, literally hundreds, if not thousands, of targets in the human proteome are amendable to hotspot-directed drug discovery.  Our team has already characterized over 130 proteins with druggable allosteric sites.

HotSpot’s Platform And Product Engine. 

At the heart of HotSpot’s discovery engine is its SpotFinder^TM platform, a series of complementary technologies that enables pan-proteome mining of putative hotspots:

• Site-mapping and virtual chemical probe techniques that prioritize potential hotspots that are predicted to bind chemical modifications (such as phosphate) and most amenable to small molecule drug discovery
• Meta-level functional data relevant to regulatory hotspots from target and gene databases, thereby creating picture of the pharmacology one anticipates through small molecules
• Natural language processing tools which scan scientific literature to identify proteins with potential regulatory hotspots where structure-function is understood in the scientific literature

Once SpotFinder^TM has identified and characterized a high potential site/protein target, Hotspot’s discovery platform combines both virtual and physical library screening to identify hits. Based on insights into hotspot binding site configurations, a novel chemical library has been created comprising privileged scaffolds/chemotypes that are not typically found in conventional screening decks. To drive specific binding modes, Hotspot applies extensive protein engineering approaches that lock the target’s hotspot in the various conformations, including those likely to enlist the optimal type of pharmacology when bound.  All of this is underpinned by state of the art computation drug design and lead optimization techniques.

HotSpot executes against this platform in a globally-distributed, virtually-integrated fashion, much like our experience at Nimbus and elsewhere. Truly proprietary elements remain purely in-house, including SpotFinder^TM, chemical library design, computational approaches like virtual screening; outsourced components are physical medicinal chemistry, assay execution, crystallography, biophysics – all performed by top tier partners around the world. HotSpot is also at the vanguard of applying technology-enabled agile startup concepts to biotech, especially with Slack as their workhorse day-to-day productivity and conferencing platform (here).

HotSpot’s Pipeline & Future Plans

As with all new drug discovery engines, target selection is a critically important driver of eventual success (or failure). HotSpot spent much of its first year mining different target classes and specific proteins in order to characterize and prioritize what ones to go after. We continue to turn the crank of the machine to identify new hotspot-possessing targets to feed the drug discovery process, and will ramp efforts to widen our pipeline over time.

To date, we’ve only disclosed two of our active programs: PKC-theta for autoimmune disease and S6K for metabolism, broadly defined. Both of these hotspot inhibitor programs appear to offer unique and differentiated pharmacology versus traditional active site inhibitors, and we’re excited by their progress.

PKC-theta is an immunokinase target offering potential to simultaneously stimulate regulatory T-cells (T-regs) while dampening effector T-cell function. Past industry attempts to drug the active ATP-binding site of PKC-theta had typically failed due to off-target effects and generally problematic chemotypes. We’ve uncovered a phosphoregulatory hotspot, previously unexploited by industry, and now have the first and only allosteric inhibitors of this kinase. These demonstrate all of the predicted attractive features (potency, selectivity, robust pharmacology), and the program is advancing into lead optimization.

S6K is a metabolic enzyme that plays pivotal role in a cell’s sensitivity to insulin signaling and changes in energy metabolism. Work by Novartis showed that knocking out S6K1 in mice restores insulin sensitivity in mice on a high-fat diet, while increasing the number/size of mitochondria. These mice were jokingly called ‘flash’ mice due to their ability to run seemingly forever. Further, S6K appears to control the metabolic energetics of cell fate, such as regulating Th17 differentiation. Because of this role, inhibiting S6K offers important applications in a range of metabolic diseases e.g. NASH, diabetes, obesity, etc…  However, the ATP/active site has never delivered selective, potent molecules with the right biodistribution properties. Using our SpotFinder^TM platform, we have found a regulatory hotspot on S6 and already identified the first and only allosteric inhibitors, with highly differentiated properties (including selectivity) versus traditional active-site binders.

The rapid progress of these two programs in less than 12-months, as well as further undisclosed target activity, gives us solid confidence that hotspot-directed drug discovery will be a rich vein to mine for unique pharmacology – and this triggered our keen interest in powering up the story with a significant Series A financing.

As is often the case, a critical input to scaling this platform will be further access to both capital and expertise over time.  To that end, we’re open to early discovery stage collaborations with partners where there’s a good fit and desire to unravel nature’s regulatory code on specific programs, as well as to long-term patient capital looking to help scale a big story.

Allosteric R&D is one of the most exciting areas of drug discovery today – but HotSpot is already turning up the heat even more.

The post Hotspot Therapeutics Turns Up The Heat On Natural Allostery appeared first on HotSpot Therapeutics.

One of the most dominant themes in tech is Agile software development, an approach that has been adopted by all of the most successful startups around the world.  Agile involves creating a simplified piece of software that is then iterated rapidly through input from real customers.  Iterations are generally short – typically one to four weeks – and are aptly called ‘sprints’.

On its face, it doesn’t appear that we in biopharma have much to learn from Agile but I believe that the philosophy behind much of the Agile Manifesto are extremely relevant to R&D heavy organizations including:

• Prototype iteration aligned with user needs
• Efficient, face-to-face communication
• Short feedback loops

Prototype iteration

Agile was a reaction against software development practices that promoted extensive upfront planning, crazy Gantt charts and code that took years to deliver.  When the software was finally delivered, it was already obsolete because the market had moved on.  In contrast, Agile emphasizes the role of learning in new product development.  This is done by creating a simple, usable piece of software, called a minimum viable product (MVP), that can be put in the hands of users to generate feedback.  This feedback then drives the next cycle of innovation.

This sounds a lot like drug discovery practiced well.  In my experience, R&D teams that make the most rapid progress are always looking to generate relevant pharmacology data as early as possible.  This may mean generating cell-based or animal data on a compound that lacks ideal potency or ADME properties.  It reflects a bias towards doing an experiment now because you can learn something important, even if it’s not the perfect experiment.  The effect of this type of thinking is to reduce the time to meaningful insight, which in many cases can dramatically change the course of a program.  In the context of clinical studies, adaptive designs and digital-health technologies may allow us to reduce the cycle time, allowing biotech to get closer to the tech ideal.

Agile demands that certain individuals serve as the voice of the customer and are tasked with ensuring the user need is always front and center.  Similarly, great drug discovery teams are razor focused on the patient right from the beginning.  This is easier in areas like oncology where patient stratification is well established but beyond this Target Candidate/Product Profiles remain an important discipline in drug discovery.

Efficient and face-to-face communication

To enable rapid product iteration, joint problem solving is critical and that’s why Agile emphasizes the need to communicate effectively with team members.

For many of us, email no longer achieves this goal.  We can receive hundreds of emails a day and it is not easy to determine which are worth our attention.  Given this, most of the tech startups I know are replacing email with a tool called Slack.  Slack is an instant messaging platform that offers important benefits over email:

• Communication is instant and frictionless leading to a free flow of ideas and creativity
• Messages are classified into subject ‘channels’ enabling the reader to decide which topic or group he/she wants to devote attention to. Moreover, there’s no need to remember to keep team members in the loop which improves transparency and alignment
• Images, files, links are seamlessly integrated into the flow of the conversation rather than as a stack of files at the end of an email that need to be downloaded. This aids comprehension and communication.

This is a typical view of Slack:

In the context of a R&D heavy biotech startup, we use Slack to communicate on all topics including sharing data and highlighting new findings from publications.  Team members can contribute their thoughts live, leading to new experimental ideas without the need to fill the calendar with more meetings.  The free-flowing nature of the discussion creates a type of virtual water cooler.

Having adopted Slack, we have now reduced our daily email volume by about 75% because conversations with internal colleagues and key external collaborators are all conducted real-time on the platform.  Our team members have noted significant gains in how connected and well-informed they feel about what is going on inside the company.  We have created a channel for each drug discovery project so team members are always in the loop on the latest thinking and data.  Private channels come into play so we can communicate with subgroups around more sensitive topics.  We also make use of channels for creative ideas #idea_generation and #random for some fun around team events, birthdays and cooking recipes.

It isn’t trivial to shift an organization to a completely new way of communicating but for us it has really been worth it.  Senior management must be fully committed to the open communication style that Slack promotes and thought needs to be put into how to organize channels.  Channels must then become the dominant way of communicating to avoid Slack devolving into a new form of E-mail.

Short feedback loops

The concept of the daily stand-up meeting originated at Borland Software and was apparently the secret sauce behind how eight people wrote a million lines of code in eight months.  It’s now viewed as a critical component of how Agile teams share information needed to coordinate activities and to remove obstacles in their way.  Stand-up meetings ensure the learning cycle remains short and maximally effective.

To be most impactful, a stand-up meeting should occur at the same time every day and everyone in the team must take part.  The format is simple: team members stand up in turn to explain what they did yesterday, what they are doing today and what’s preventing them from doing their job.

We have established a similar practice at HotSpot with a thirty-minute meeting that occurs every day at 8.30am.  Everyone in the team has their minute or two to speak.  The effect of the daily meeting is to give our team a regular heartbeat.  Everyone comes out of stand-up knowing the most important thing they need to accomplish in a day.  For many of us, the stand-up meeting is a highlight of the day, an opportunity to connect with colleagues and celebrate a success or rally around a collective problem.  With a group of inquisitive scientists, the potential for distraction by cool data is high, so discipline is needed to make sure any deeper discussion topics are taken off line.

Admittedly, there are limits to which the Agile analogy can be applied within biotech since writing code isn’t the same as designing molecules and the regulatory environment is far more onerous in pharma.  For example, it is not possible to make small tweaks to a molecule in a phase 3 study.  However, biotech and tech companies do share a common desire to create the best environment for innovation and I am certain that we will be able to learn more from our tech colleagues in the years to come.

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