Engineering Biotech 2.0
Related Sciences (RS) builds a new class of risk-optimized biotech companies and a unique suite of platform capabilities to design and efficiently manage them.
By re-envisioning every aspect of the biotech operating and investment model from the ground up, RS systematically mitigates the biggest historical sources of risk and inefficiency to create a new, better way to invest successfully in the discovery of important new medicines for patients.
Better Model Needed
The traditional biotech model’s poor risk profile drives high failure rates and constrains investment, slowing humanity’s pace of progress against all diseases.
High Intrinsic Risk of Failure
Over 90% of all drugs fail in the clinic 1 and almost 85% of all biotechs fail to create any economic value. 2
Very Few Biotech Investors
99.7% of the world’s institutional investors have effectively zero exposure to therapeutics biotech. 3
Major Promise Left on the Table
Over 75% of target-disease opportunities with strong evidence of links, have never been tried: 23,000+ opportunities spanning 3,800+ individual targets for 4,200+ diseases. 4
Why is biotech so risky?
Risk in therapeutic development is fundamentally shaped by a discrete set of inter-related dimensions. RS spent years researching the top biotech operators and investors to elucidate the drivers of risk and inefficiency in biotech and the best ways to mitigate them.5
Poor Opportunity Selection
Biotech companies are often created to advance a specific area of research or technology, but without any form of objective comparison of that opportunity's risk-reward characteristics contextualized against others. As a result of this passive form of scientific bias, many companies unknowingly expose themselves to built-in risks that can materially reduce their long-term odds of success. For example, programs with strong human genetics evidence of links to a disease have historically achieved 3x+ higher rates of clinical success than those that do not, and structurally enabled targets with validated assays hold objectively greater odds of efficient, successful drug discovery than those without. Biotechs can increase their odds of success by prioritizing only those opportunities that benefit from superior objective characteristics which have historically predicted success, and avoiding those with features that empirically increase difficulty or failure risk.
Binary Outcomes
It is widely understood that the odds of clinical success in developing a new medicine are very low, with cumulative probabilities of failure before regulatory approval as high as 90-95%. However, faced with scarce resources, most biotech companies must opt to approach clinical development sequentially with a "lead asset" out in front and a small overall portfolio of clinical programs, creating high empirical probabilities of total loss. If biotech companies instead optimized for avoidance of bad binary failure risks, a range of alternate portfolio design, partnering, and exit strategies principles emerge which can help to improve overall risk-adjusted expected returns, by constraining certain traditional choices. From a decision theory perspective, several of these risk-optimization strategies also create a win-win outcome for both biotech and pharma, suggesting that new value creation paradigms in biotech are wholly possible.
Inefficient Company Designs
While each biotech focuses on a different research area, a significant portion of capital is often allocated to building very similar scientific and operating capabilities over and over again. From investing in core laboratory space and equipment, to recruiting generalist researchers, to all business development, accounting, and back office functions, and even including many types of common specialist advisors, a significant portion of most biotechs' investments are into fundamentally similar capabilities. As compared to models which better share capabilities or spread these investments across multiple companies, this default exposes biotechs to high fixed capital costs, rapid technology obsolescence risks, and material scalability constraints, while also creating negative economies of scale for the industry as a whole. Further, most biotechs continue to focus on local staffing within in "biotech hubs" instead of exploring newer models to overcome geographic constraints in engaging with the very best scientific talent. New innovations in company designs offer a range of stackable opportunities to enhance efficiency, flexibility, and scientific quality.
Insufficient R&D Capabilities
The therapeutics biotech sector benefits from broad and near-continuous cycles of technology innovation, as ever-improving drug discovery technologies, research tools, computational capabilities, and therapeutic modalities are invented and then quickly democratized. As a result, at any point in time, for virtually all biotechs, a range of important incremental advantages to their pace, costs, research goals, and/or overall odds of success, are likely on offer somewhere. For this reason, biotechs should increasingly focus on integrating a much wider range of technology advantages across many different aspects of the research, discovery, and development process, instead of purely focusing on building deep-narrow technology advantages. The availability and pace of technological change suggests the high value of adopting new hybrid internal-external or fully virtual R&D models powered by multiple external specialist vendors alongside, or even instead of, a sole reliance on internal capabilities and expertise.

An Innovative New Model
to Mitigate the Key Risks
RS combines innovations across four complementary areas to systematically mitigate biotech’s biggest historical sources of risk and efficiency and create of a new optimized class of therapeutics biotech company with superior risk-reward characteristics.





Opportunity Selection
RS FacetsTM is a unique, multilayered data science platform designed to ingest all activities in global biomedicine and enable RS to identify the top 0.001% of all opportunities on an unbiased, quantitative risk-reward basis.
RS Facets™️
Prioritization Engine




Time-Resolved Biomedical Atlas
RS curates, cleans, integrates a large number of public and private data sets covering virtually all activities in global biomedicine, from early basic research through to successful value creation, into a single 80+ billion datapoint, time-resolved, relational database.

Quantitative Opportunity Ranking
Leveraging the comprehensive data contained in the Biomedical Atlas, RS then ranks each of 100s of millions of possible drug discovery opportunities across hundreds of quantitative metrics to enable unbiased risk-reward comparisons to be made. By algorithmically recapitulating all of the risk-reward considerations a seasoned pharma drug hunter or biotech VC evaluates when considering a new drug program (but at superhuman scale and speed) RS is uniquely positioned to identify the top 0.001% of all opportunities at any point in time.
Predictive Machine Learning
With the benefit of these two powerful and proprietary data layers, RS is uniquely positioned to build advanced machine learning and statistical inference models that can discover unfolding trends and hidden relationships to make data-driven predictions about the future, which RS can leverage to identify the programs, portfolio designs, and research areas with the greatest odds of clinical success and economic value creation.

Value Creation Strategy
The study of biotech's historical clinical and economic success and failure rates, contextualized against both its key risk inputs (capital and time) and reward outputs (returns on investment), suggests a set of evidence-based investment principles which can optimize a biotech's overall risk-adjusted expected returns while also minimizing its exposure to bad binary failure risks.

Preclinical Specialized
RS specializes in and aims to maximize its companies' capital exposure to preclinical drug discovery — the first 3-5 years of R&D during which a new drug is created, refined, and readied for human clinical trials — because it has, by far, the best risk-adjusted value creation profile in biotech.

Portfolio-Based Discovery
Instead of accepting high binary failure rates from a “lead asset” as an inevitable feature of drug R&D, RS builds conservative levels of managed attrition directly into its business model. By assembling large portfolios of top drug programs with biological relationships that create both cost and knowledge synergies, and investing in parallelized instead of sequential drug discovery, RS companies can generate superior risk-adjusted expected returns inclusive of inevitable program attrition, while accelerating timelines and increasing economies of scale.

Early Exit Optimization
Despite a sector-wide focus on clinical success, preclinical discovery is the stage that garners both the majority of all asset acquisitions and the highest median risk-adjusted returns on capital. RS optimizes each aspect of its model—from program selection to team design to R&D quality and clinical strategy development, to its pharma partnering models—to collectively maximize its chances of being able to sell completed preclinical drugs early at attractive returns on capital, before toxic mid-clinical failure risks take hold. Further, by placing each individual drug program in its own HoldCo, RS can (tax-efficiently) sell off individual drug programs without selling its operations.

Company Architecture
RS combines a novel decentralized scientific staffing model, a fully virtual operating model, synergy-optimized portfolio constructions, and asset-centric corporate structures, to significantly reduce costs and capital expenditure requirements while enhancing its companies' scientific depth, breadth, pace, and flexibility.

Decentralized Team Science
RS assembles field-leading academic scientists and clinicians from around the world into big, collaborative teams tailored to the unique expertise needs of each individual drug program, to work alongside RS and industry drug discovery specialists to craft best-of-breed R&D strategies. This “fractional CSO” model replaces the traditional combination of full-time generalists and passive quarterly “Scientific Advisory Board” members, with a more engaging team science model that yields both superior science and dramatically improved cost efficiencies.

Data-Designed Portfolios
RS leverages its unique data platform to design “Constellations” – large portfolios of its highest-ranking drug discovery opportunities, which all share biological connections to research areas of ascendant commercial interest. Because RS Constellation designs balance both individual program attributes and portfolio-level risk-reward profiles, RS can construct portfolios that maximize cost and knowledge efficiencies and multi-risk diversification, while investing only in programs with the very best risk-reward profiles.
Fully Virtual Operating Model
RS Constellations operate leanly over a shared suite of RS capability platforms that provide access to the specialized interdisciplinary staff, labs, and technologies needed for world-class drug discovery without requiring any incremental overhead investments. By eliminating biotech restaffing requirements, capital-intensive lab build-outs, and geographic constraints, RS can build companies that leverage the best talent and capabilities wherever they may live while dramatically reducing fixed overhead and non-R&D uses of capital.

Technology Advantages
RS continually builds and centralizes access to the latest discovery, therapeutic, and translational technologies to maximize its companies' odds of efficient, successful discovery of valuable new medicines.

Strategic Partnerships
RS curates and establishes long-term strategic partnerships with the best contract research organizations, discovery platforms, and technology companies in the world to provide its Constellations with access to specialized capabilities, at preferred cost structures, and with uniquely aligned incentives for quality and success. RS works with numerous partners, including drug discovery leader, Evotec SE, which provides RS with access to its global network of cutting-edge laboratories, multi-modality screening capabilities, and 1,000’s of expert drug discovery scientists, under a collaborative risk-and-reward-sharing model.
Discovery Technologies Platform
Drug discovery technologies continually advance as new high throughput screening and robotics systems, compound libraries, in vitro and in vivo models, reagents, assays, and analysis methods, and in silico/AI drug design tools all improve and compound on each others' benefits. RS continually curates and centralizes access to the latest discovery technologies via leading vendors and in-house capabilities, to maximize its companies' end-to-end discovery efficiency, pace, quality, and overall odds of success.
Therapeutic Technologies Platform
RS is modality agnostic; we view each distinct therapeutic modality as a unique tool with certain biological benefits that are ideal for a set of therapeutic objectives, but not others. RS is currently equipped to create more than 10 different types of medicines, combining curated external vendor relationships with expert modality consultants, delivery specialists, manufacturing partners, and in some cases such as RS' mRNA labs in Cambridge, even internal specialized wet lab buildouts. By continually expanding its access based on new advances in each therapeutic technology, RS aims to ensure each drug program to benefit from the broadest possible set of tools to achieve ever-improving efficacy, safety, delivery, and cost profiles.

Clinical Translation Platform
RS leverages its Facets™ data platform to systematically evaluate multiple lines of human and preclinical evidence in order to build “a pipeline” of valuable disease applications into each of its drug products, and prioritize programs with the most transformational prospective clinical benefit. By building a variety of clinical paths into its experimental plans spanning common, rare, and precision disease segments, RS can maximize each program's aggregate potential clinical value and flexibility for potential acquirers. By prioritizing programs with distinctively strong human lines of evidence, and then leveraging next-generation predictive toxicology, human-like in vitro tools, CRISPR screens, and genetically-engineered animal models during translation, RS delivers best-in-class drug packages that can maximize acquirer confidence in each program's expected clinical efficacy and safety profile.
A better biotech model benefits everybody
Biotech's overall success and efficiency rates directly determine the new medicines humanity is able to benefit from. As such, all of biomedicine's stakeholders can benefit from improvements in biotech's operating and investment model that increase its successful shots on goal or reduce its time- talent- and financial- waste along the way:
Patients
A greater number of transformational new medicines for virtually all types of disease are discovered sooner and successfully brought to market more often.
Researchers
Breakthrough research is successfully translated into medicines more often, and our new model enables leading researchers to contribute to biotech R&D without needing to leave academia.
Entrepreneurs
Biotech company failure is significantly reduced, driving significantly lower job turnover and training a deeper bench of capable biotech operators for the next generation of drugs.
Pharma
Supply of high quality, clinic-ready drugs is dramatically increased and made available at acquisition prices that both incentivize biotech and are sustainable for pharma’s own economic models.
Investors
Better biotech risk-reward drives a much greater proportion of investor wins broadening interest among institutional investors to fund future biotech innovations.
Patients
A greater number of transformational new medicines for virtually all types of disease are discovered sooner and successfully brought to market more often.
Researchers
Breakthrough research is successfully translated into medicines more often, and our new model enables leading researchers to contribute to biotech R&D without needing to leave academia.
Entrepreneurs
Biotech company failure is significantly reduced, driving significantly lower job turnover and training a deeper bench of capable biotech operators for the next generation of drugs.
Pharma
Supply of high quality, clinic-ready drugs is dramatically increased and made available at acquisition prices that both incentivize biotech and are sustainable for pharma’s own economic models.
Investors
Better biotech risk-reward drives a much greater proportion of investor wins broadening interest among institutional investors to fund future biotech innovations.
Team
RS is led by a multi-disciplinary team of drug discovery, data science, and venture capital veterans who have founded, built, and led biotech companies worth billions, and is backed by a luminary group of investors.
























Companies
RS builds, launches, and manages one new company every 1-2 years, each of which is comprised of a “constellation” of the top 0.001% of all drug opportunities in an ascendant area of research and commercial interest.
Danger Bio focuses on danger detection—the “alarm system” inside of every cell which alerts the immune system to threats like infection and injury, and when imbalanced, can drive autoimmunity or cancers

Currently in design and assembly phase. Research focus not yet disclosed.
