Version 1.7.2 · May 2026

DiPS is the first professional society that aims to collaboratively leverage the power of digital capabilities to address persistent challenges in drug development. We start with the biology problem. Preclinical evidence has helped deliver many effective medicines, but it is not clinically predictive, economical, or timely enough for the decisions modern drug development now demands. Too often, the evidence misses or underuses the dynamic biology that determines whether a therapy will translate: disease progression, treatment response, safety signals, animal welfare signals, and adaptive-to-maladaptive inflection points. DiPS advances the application of digital approaches to make that evidence more predictive, reproducible, integrable, regulator-grade, and clinically translatable, so the right therapies reach the right patients sooner.

Digital preclinical science is the discipline of applying digital approaches, including continuous monitoring, AI-derived readouts, and structured-data integration, to make preclinical evidence more predictive, reproducible, and regulator-grade.

Operationally, digital preclinical science is the application of digital measures, digitally enabled data capture, computational methods, AI/ML, structured metadata, and evidence standards to generate, transform, integrate, interpret, and learn from preclinical evidence across in vivo, in vitro, ex vivo, in silico, and historical study contexts.

Digital preclinical science is platform-neutral and modality-neutral. It applies equally to evidence generated from animal studies, from non-animal methods including microphysiological systems and organ-on-chip platforms, organoids and complex in vitro models, computational toxicology, QSAR, read-across, and adverse outcome pathway frameworks, and to historical evidence in any of these modalities. The application layer is the same. The decision logic is the same. The evidentiary criteria are the same. What changes is the context of use, and what each modality can and cannot tell us about the decision at hand.

This is an explicit choice. The field has spent a decade caught in a binary debate between animal models and non-animal methods. DiPS takes no side in that debate. We treat both as legitimate evidence inputs that must demonstrate fit-for-purpose evidence for the specific decision they are asked to support. Neither modality is privileged by default. Neither is dismissed by default.

DiPS does not seek to own every sensor, algorithm, platform, dataset, data standard, AI model, organ-on-chip system, in-silico method, or digital pathology approach used in preclinical development. DiPS focuses on how digital approaches are selected, supported by fit-for-purpose evidence, integrated, interpreted, and used to improve specific preclinical decisions, regardless of whether the underlying evidence is animal-based or non-animal.

DiPS does not validate digital measures, sensors, AI models, platforms, data standards, microphysiological systems, in-silico methods, or non-animal assays. DiPS defines how fit-for-purpose evidence, from any of these sources, is assembled, interpreted, and connected to preclinical decisions.

DiPS is not an AI society, a vendor consortium, a digital pathology society, an MPS society, an in-vivo society, an in-vitro society, or a general enterprise digitization program. DiPS begins with the decision. We define the biological, translational, animal welfare, or regulatory problem first. Then we determine whether a digital approach can improve the evidence supporting that decision, and which modality, or combination of modalities, is fit for that purpose. This is how DiPS avoids building hammers and then looking for nails.

DiPS treats prior experience as evidence, not noise.

Failed translation, negative studies, weak endpoints, unexpected toxicity signals, animal welfare observations, mechanistic data from non-animal assays, historical controls, NAM validation studies, and near misses should become structured learning assets rather than institutional memory trapped inside individual organizations. The field should learn from its past experiences with the same discipline it applies to what it wants to discover next.

Digital preclinical science is not only about generating new data. It is about learning better from the data, decisions, failures, and biological signals the field has already produced, across both animal-based and non-animal modalities, and making that learning usable before the next program repeats the same mistake. DiPS will help shift drug development from a regimented series of progressions toward an iterative, continuous-learning process.

DiPS is launching at a specific regulatory moment, and the framing of the society reflects that moment honestly.

The U.S. FDA Modernization Act 2.0 (2022) and the FDA Food and Drug Omnibus Reform Act (FDORA, 2022) added non-animal alternatives to the list of testing modalities that the FDA may consider in support of investigational new drug submissions. They did not remove animal testing requirements. They expanded the menu, and they made context-of-use evidence the deciding factor between modalities.

The European Medicines Agency's reflection paper on the use of New Approach Methodologies in regulatory submissions, the EMA 3Rs Working Party, and parallel work at the OECD on Integrated Approaches to Testing and Assessment (IATA) and at ICH have moved in the same direction. The U.S. National Institutes of Health Office of Research Innovation, Validation, and Application (ORIVA) and the FDA's Innovative Science and Technology Approaches for New Drugs (ISTAND) pilot are creating regulator-proximate pathways for non-animal methods.

The result is not the end of animal testing, and it is not a wholesale switch to non-animal methods. The result is that every modality, animal-based or not, must now demonstrate fit-for-purpose evidence for the specific decision it is asked to support. That is the regulatory ground on which DiPS operates.

DiPS exists in part because this regulatory shift creates a coordination problem that no existing professional society fully owns. The application layer, where modality choice meets digital methods meets context of use meets clinical intent, is where DiPS leads.

DiPS begins with four connected application areas. These are not standing working groups or permanent domains. They are the initial scope boundaries for a public-facing professional society focused on application. Each area applies symmetrically to animal-based and non-animal evidence.

1. Evidence integration and decision frameworks. Methods that connect pharmacology, efficacy, safety, welfare, pathology, mechanistic data from non-animal assays, exposure-response, disease biology, adverse outcome pathways, and clinical intent into decision-ready preclinical evidence packages. Evidence from animal studies and from non-animal methods, including microphysiological systems, organoids, and computational toxicology, is integrated under the same decision logic.

2. Context-of-use evidence requirements and regulatory confidence. Fit-for-purpose evidence logic, auditability, lifecycle management, quality expectations, and regulator-grade templates that help digital preclinical evidence be understood, challenged, and used appropriately, regardless of whether that evidence is generated by an animal model, an MPS platform, an organoid system, an in-silico method, or a hybrid approach. Context-of-use is the governing framework in either direction.

3. Dynamic biological monitoring and novel digital evidence. Continuous or frequent measurement of biology, including behavior, physiology, disease modulation, exposure-response, safety progression, treatment response, and adaptive-to-maladaptive transitions in animal models, and including time-resolved imaging, multi-electrode array recordings, organ-on-chip readouts, perfusion dynamics, organoid morphometric and functional change, and live-cell mechanistic readouts in non-animal systems.

4. Computational transformation and reuse of existing evidence. Digital pathology outputs, historical study data, structured metadata, SEND-like efficacy datasets in animal-based work, and equivalent data structures for non-animal methods, including the U.S. EPA ToxCast and Tox21 databases, EURL ECVAM databases, the OECD QSAR Toolbox, and emerging data structures for microphysiological systems and organ-on-chip platforms. Study reconstruction and reusable evidence packages span both modalities.

DiPS does not replace DIVA, Pistoia Alliance, or any adjacent society. DiPS does not replace the bodies that lead non-animal method validation either.

DIVA is the natural lead for development, validation, adoption, and regulatory acceptance of digital measures in preclinical in vivo research. DiPS does not duplicate that role. DiPS does not validate digital in vivo measures. DiPS connects validated or validation-ready digital measure outputs to broader preclinical decision frameworks, animal welfare, metadata, regulatory confidence, and clinical translation.

Pistoia Alliance is the natural lead for precompetitive data standards and metadata infrastructure in life sciences, including SEND-like in vivo efficacy data standards and minimal metadata work for repurposing non-clinical in vivo data. DiPS does not duplicate that role. DiPS focuses on the application layer: how those standards are used to support specific preclinical decisions, connect digital measures with biological interpretation, clarify evidence requirements, and assemble regulator-grade evidence packages.

DiPS does not duplicate the work of organizations leading non-animal method validation, adoption, and regulatory acceptance. The Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) lead in the United States. The European Union Reference Laboratory for alternatives to animal testing (EURL ECVAM) leads in Europe. The European NAMs research clusters, including ASPIS and ONTOX, lead on integrated mechanistic frameworks. The European Organ-on-Chip Society (EUROoCS), the IQ Consortium MPS Affiliate, and the Center for Alternatives to Animal Testing (CAAT) lead on microphysiological systems and broader alternatives science.

DiPS does not validate non-animal methods. DiPS connects validated or validation-ready non-animal method outputs to broader preclinical decision frameworks, regulatory confidence, and clinical translation, in the same way it connects validated digital in vivo measure outputs.

3Rs Collaborative and FELASA lead on replacement, reduction, and refinement. DiMe is the home for clinical digital measures. CDISC leads on clinical and non-clinical data standards. SOT, ESTIV, LASA, AAALAC, and other discipline-specific societies each convene their own discipline. None of them alone owns the intersection where digital preclinical science actually happens. DiPS connects their outputs into application-focused, decision-ready preclinical evidence frameworks.

The goal is not another silo. The goal is collaborative and digital continuity across existing silos, on both sides of the animal-based / non-animal divide.

Digital preclinical science founded in problem definition, integration, and continuous learning.

Leveraging digital approaches to address persistent challenges in drug development, so the right therapies reach the right patients sooner.

To advance the application, integration, and regulator-grade use of digital preclinical evidence, both newly generated and computationally transformed, across animal-based, non-animal, and in-silico modalities, to better capture dynamic biology, improve preclinical decisions, reduce weak translation, retire any modality use that does not contribute fit-for-purpose evidence to the decision at hand, and help effective therapies reach patients sooner.

1. We start with the decision and the patient-relevant problem. DiPS does not build hammers and then look for nails. We define the biological, translational, animal welfare, or regulatory decision first. Then we ask which digital approach, and which modality, animal-based or non-animal, if any, improves it.

2. We treat prior experience as evidence. The field should learn systematically from previous studies, failed translation, negative results, weak endpoints, unexpected toxicity, welfare observations, mechanistic data from non-animal assays, NAM validation studies, and historical datasets. Prior experience is evidence, not noise. Learning from what already happened is as important as generating the next dataset.

3. We are application-first, not technology-first and not modality-first. No platform and no modality gets privileged because it is new, digital, AI-enabled, animal-based, non-animal, or commercially compelling. Fit-for-purpose evidence wins. The decision wins.

4. We lead the connective layer. Where others own a domain, we connect and strengthen it. Where the field lacks a bridge across measures, metadata, evidence requirements, regulation, modality choice (animal-based, non-animal, in-silico), animal welfare, and clinical translation, DiPS leads.

5. We build regulator-grade ways of working from day one. FDA, EMA, ICH, OECD, PMDA, and other public authorities are engaged early and appropriately. Regulatory expectations are designed with, not worked around. DiPS does not imply agency endorsement. It builds evidence packages that are transparent, auditable, context-of-use specific, and regulator-proximate. In this charter, “regulator-grade” means transparent, auditable, context-of-use specific, lifecycle-managed, and supported by fit-for-purpose evidence. It does not mean pre-approved, endorsed, or accepted by any agency.

6. We disrupt when disruption benefits the patient, the science, or the decision. We retire methods when evidence demands it. We dissent when current practice is comfortable but wrong. We name what is broken when naming it helps the field build something better. We hold ourselves to refinement, reduction, and replacement wherever an animal-based modality is involved, and to context-of-use validation wherever a non-animal modality is. The duty to demonstrate fit-for-purpose evidence cuts both ways.

7. We are one table. Pharma, regulators, CROs, academia, the 3Rs and NAMs communities, microphysiological systems and organ-on-chip researchers, computational toxicologists, digital technology providers, data standards organizations, and adjacent professional societies all belong at the table. No single voice carries structural advantage. Biases are mitigated and final products strengthened through multi-disciplinary collaboration.

Preclinical evidence has helped deliver many effective medicines and kept most patients safe. It is not broken. But it is not clinically predictive, economically efficient, or timely enough to support the decisions modern drug development now demands. Too often, promising pharmacology reaches the clinic with gaps in how disease progression, treatment response, safety signals, animal welfare, mechanistic biology in non-animal systems, and adaptive-to-maladaptive inflection points were measured, connected, and interpreted. The cost is counted in failed molecules, delayed therapies, regulatory rework, modality use that did not contribute fit-for-purpose evidence (whether animal-based or non-animal), and patients who run out of time.

DiPS exists because we have a biology problem amenable to a technology solution, one that digital approaches can help address by making preclinical evidence more continuous, quantitative, reusable, regulator-grade, and clinically translatable, regardless of which modality produces the evidence.

The reason is not that one tool is missing. Target discovery, pharmacological hit identification, and drug modality engineering have advanced dramatically, but our approaches to modeling pharmacology, efficacy, safety, disease progression, and treatment response have not advanced at the same pace. This is a biology problem first. It is a problem that digital approaches can help address when they enable us to monitor biological responses and perturbations more dynamically, identify key inflection points, produce more quantitative outputs, and provide a better line of sight to our intended patients, in animal models, in microphysiological systems, in organoids, in in-silico simulations, or in any combination of these.

It is also a compound failure. Evidence is too discontinuous to capture biological dynamics. Endpoints are surrogate or weakly aligned to clinical intent. Metadata is incomplete. Validation is inconsistent across modalities. Historical evidence is not reusable. Decision-makers cannot easily integrate evidence across pharmacology, efficacy, safety, welfare, pathology, mechanistic readouts from non-animal assays, exposure-response, and patient-relevant disease biology.

Many stakeholders, including regulator-adjacent scientists and public authorities, increasingly recognize the need for shared expectations, structured evidence, transparent methods, and fit-for-purpose standards across modalities.

This does not mean every digital approach is useful. It does not mean every non-animal method is fit-for-purpose, or that every animal model is. It means the right approach, applied to the right biological question, with adequate context-of-use evidence, can expose evidence that current methods miss.

The gap is not “we need another tool.” The gap is structural. It sits in application, endpoints, metadata, evidence requirements, regulatory confidence, study design, modality choice, learning loops, and the incentives that shape all of them.

What has been missing is not another technology platform. A novel transdisciplinary collaboration that operates across animal-based and non-animal modalities, across digital and traditional methods, has been missing. DiPS is that collaboration.

The long-term measure of DiPS success will be whether preclinical evidence becomes more clinically relevant and predictive. That measure takes ten to fifteen years to register. DiPS therefore holds itself to nearer lead measures that show whether the field is changing before clinical outcomes can prove it.

These are the three problems we were founded to address, and the three commitments we make in return.

Endpoints are weak or surrogate. Metadata is incomplete. Evidence requirements are inconsistent across animal-based and non-animal methods. Regulatory expectations evolve. Historical evidence is underused. Evidence streams are difficult to integrate within a modality, and even harder to integrate across modalities. The gap is structural, not a matter of any single platform.

The deeper problem is that current evidence is often not the right evidence, not complete enough, not in a reusable form, not aligned closely enough to clinical intent, or not supported by sufficient quality standards. This is true whether the evidence comes from an animal study, a microphysiological system, an organoid, or an in-silico method.

Digital approaches do not solve this automatically. They help only when they are applied to specific biological, translational, welfare, or regulatory failure modes, with the right modality choice.

Our commitment. We codify the form, use, and evidentiary logic of fit-for-purpose preclinical evidence, with shared vocabulary, shared standards, context-of-use expectations, evidence requirements, and evidentiary criteria legible to regulators and decision-makers, applied symmetrically to animal-based and non-animal evidence.

When existing endpoints are sufficient, we help make them computationally accessible, reusable, and decision-ready. When existing endpoints are not sufficient, we help define the decision context, evidence requirements, modality choice, and integration logic for novel digital endpoints and digital measures, with context of use and clinical intent made explicit from the start.

Digital in vivo measures, metadata standards, animal welfare, NAMs, microphysiological systems, organoid science, computational toxicology, AOP frameworks, digital pathology, AI/ML, clinical digital measures, toxicology, efficacy, pathology, and regulatory science are developing in parallel.

The problem is not that relevant organizations are absent. The problem is that the links across them are weak. Digital continuity across preclinical evidence (animal-based and non-animal), animal welfare, NAMs, MPS, data standards, context-of-use evidence requirements, regulatory confidence, and clinical translation has not had a clear professional home.

Our commitment. DiPS does not replace DIVA, Pistoia, 3Rs Collaborative, DiMe, CDISC, ICCVAM, NICEATM, EURL ECVAM, ASPIS, ONTOX, EUROoCS, the IQ MPS Affiliate, CAAT, or discipline-specific societies. We partner with these bodies where they already lead, and we lead the application layer where no single body owns the problem.

DiPS codifies the links across them: shared definitions, use cases, metadata logic, evidence requirements, regulator-grade evidence packages, and learning loops that make digital preclinical science usable across modalities. We build digital continuity across existing silos.

The field too often begins with a preferred platform (AI, sensors, digital pathology, NAMs, MPS, organoids, in-silico methods, or animal models) and then argues for its adoption. That reverses the order of the work.

The harder question comes first. What decision must be improved. What evidence is missing. What context of use applies. Which modality, alone or in combination, is fit for that purpose. And does the result predict clinically meaningful biology.

The binary argument between NAMs and animal research consumes energy that belongs to the harder question: is the evidence fit for purpose, and does it predict. DiPS rejects that binary. Both modalities have legitimate uses. Both have failure modes. The right question is the decision, not the modality.

DiPS transcends individual platforms and individual modalities. It asks how each approach contributes to a stronger, more dynamic, more humane, and more clinically aligned evidence package.

Our commitment. We take no ideological side. We evaluate every platform and every modality, whether digital, in vivo, in vitro, ex vivo, in silico, AI-enabled, MPS-based, organoid-based, AOP-derived, or historical-data-derived, against one standard: context of use, fit for purpose, supported by adequate evidence for the decision, and predictive of clinically meaningful biology.

We define the field. We publish the body of knowledge: definition, scope, taxonomy, vocabulary, decision contexts, context-of-use expectations, fit-for-purpose evidence requirements, metadata-use expectations, and regulator-grade templates. The body of knowledge applies symmetrically to animal-based and non-animal evidence.

We do not certify. We set the reference standard for application.

We bring the multi-stakeholder, multi-disciplinary table together and keep it together. Scientists, regulators, sponsors, CROs and CDMOs, academia, the 3Rs and NAMs communities, microphysiological systems and organ-on-chip researchers, computational toxicologists, digital technology providers, data standards bodies, and clinical digital measure experts.

We begin with a focused founding forum series: aligned presenters, moderated roundtables, and practical reading sessions that create exposure, recruit partners, and align the field around the definition, opportunity map, and first-year products.

We run precompetitive working groups only when the problem, value proposition, partner base, and output are sufficiently defined.

We publish open-access resources: handbooks, templates, benchmark datasets, case studies, failure reports, evidence examples, decision frameworks, and foundational papers. We address modality-specific questions only where they bear on application; we address application-layer questions across modalities by default.

We surface what fails as readily as what works. We build the learning culture this discipline has lacked. Prior experience treated as evidence, not noise.

Every DiPS output is designed to help the field learn faster from both new evidence and prior experience.

We also publish The Digital Record, a weekly public-facing record of what DiPS published, what we partnered on, what we learned, and what we changed our minds about.

Year one is not a working-group year. Year one is a field-definition year.

Low risk to early partners. Defined deliverables. No premature scaling.

DiPS will focus on four foundational products.

1. Definition and scope statement. A concise definition of digital preclinical science, including what DiPS owns, what it does not own, how DiPS treats animal-based and non-animal modalities under one application-layer framework, and how DiPS differs from adjacent organizations such as DIVA, Pistoia Alliance, DiMe, CDISC, 3Rs Collaborative, ICCVAM/NICEATM, EURL ECVAM, ASPIS, ONTOX, EUROoCS, the IQ MPS Affiliate, CAAT, and discipline-specific societies.

2. Opportunity and value proposition map. A discrete list of high-value opportunities where digital approaches can improve preclinical-to-clinical translation, animal welfare, reproducibility, data reuse, decision quality, or regulatory confidence, across animal-based and non-animal modalities. The map will identify which stakeholders benefit, what problem is being solved, what evidence gap exists, and what would make the opportunity worth funding.

3. Founding forum series. A focused public-facing virtual forum series with aligned presenters, moderated roundtables, and practical reading sessions. The presenter mix and the topic mix are deliberately balanced across animal-based and non-animal modalities. The goal is exposure, recruitment, and alignment around the definition, opportunity map, and first-year products. The founding forum series is not a broad annual meeting. It is an initiating event to test whether the field recognizes the same problem and wants the same discipline.

4. Foundational gaps and opportunities manuscript. A publishable concept paper, provisionally titled Digital Preclinical Science: Gaps, Opportunities, and Evidence Standards. The manuscript will define the discipline, explain why current preclinical evidence often fails to translate (across modalities), identify priority digital opportunity areas, define the application layer, and propose near-term lead measures. This is the paper that earns the meeting.

Funded working groups begin only after these four products show where partner demand and field value are strongest.

Seven non-negotiables define how DiPS operates.

1. Application before technology, application before modality. Every DiPS project begins with a defined decision, context of use, and evidence gap. Technology selection comes later. Modality selection comes later. A project that cannot name the decision it improves does not proceed.

2. Precompetitive, by default. Working groups are structured so sponsors co-fund shared challenges upstream of product competition. Progress at this scale is precompetitive, or it does not scale. In year one, this means a low-risk founding partner model before full working-group sponsorship.

3. Open-access, by default. Every DiPS consensus output, whether a handbook, template, standard, benchmark, or concept paper, is free to read, cite, and adopt. Members fund the work. The field uses it.

4. Regulator-proximate, by design. Regulators participate free of charge where agency rules permit. Standards and templates are drafted with regulatory input early, not presented after the fact. Regulatory participation is advisory and non-commercial. DiPS does not imply endorsement by any agency. The goal is regulator-proximate work, not regulatory theater.

5. Firewalled, always. Sponsors may fund working groups. Sponsor-employed scientists may contribute expertise. No sponsor controls the output. Consensus methods, authorship rules, conflict management, and public release terms are defined before work begins.

6. Global, member-based, cross-Atlantic by design. Parallel US and EU legal entities ensure no single jurisdiction or regulator owns disproportionate influence.

7. Living standards, not frozen standards. Digital methods evolve faster than drug development timelines. Non-animal methods evolve faster still. DiPS standards are versioned, updateable, and lifecycle-managed across modalities. The goal is not to freeze a device, an algorithm, an organ-on-chip platform, or an animal model. The goal is to preserve evidentiary meaning, equivalence, traceability, and context of use as technology and modality choice evolve.

Founding culture note. DiPS should stay human. At DiPS social gatherings, Dipping Dots are encouraged. Serious science does not require sterile community rituals.

Six gates make the next investment decision easier. Foundation before scale.

1. Months 1 to 3. Founding alignment. Charter v1.7.2 approved. Founding board recruitment begins, with deliberate balance across animal-based and non-animal expertise. Interim governance, conflict rules, and scope boundaries are ratified. A small founding partner circle is recruited at low cost and low risk.

2. Months 3 to 4. First founding forum session. DiPS convenes the first session of the founding forum series. A focused virtual event with aligned presenters across both modalities, a moderated roundtable, and a practical reading-group component. The goal is exposure, recruitment, and alignment around the four first-year foundation products.

3. Month 6. Definition and scope statement. DiPS publishes its working definition of digital preclinical science, its operating boundary, its non-replacement position relative to DIVA, Pistoia, ICCVAM, NICEATM, EURL ECVAM, EUROoCS, IQ MPS Affiliate, and CAAT, and its initial map of adjacent organizations.

4. Month 9. Opportunity and value proposition map. DiPS publishes a ranked list of digital preclinical opportunities, including stakeholder value, feasibility, decision impact, modality balance, and potential partner demand. DiPS also defines partnership pathways with adjacent organizations on both the in-vivo and the non-animal sides, with clear non-overlap and candidate co-branded outputs.

5. Month 12. Foundational manuscript submitted. DiPS submits the foundational gaps and opportunities manuscript. This is the first credibility product. The paper that earns the meeting.

6. Months 18 to 24. Funded execution begins. Only after the foundational year does DiPS launch funded working groups. Two or three work packages are prioritized based on partner demand, regulator relevance, feasibility, modality coverage, and field impact.

Four decisions are ratified in the founding period. None is revisited without board supermajority.

Co-chairs Stefano Gaburro, PhD, Europe-based industry scientist and translational digital biology leader, spanning neuropharmacology, preclinical neuroscience, laboratory animal welfare, 24/7 home-cage monitoring, digital biomarkers, minimal metadata standards (Pistoia MNMS Working Group), the integration of animal-based and non-animal modalities, and the application of digital technologies to improve preclinical model relevance and reproducibility (EU), and Szczepan Baran, MS, VMD, Chief Scientific Officer at Instem and founder of Baran Café, spanning digital health, AI, NAMs, translational safety, animal welfare, regulatory science, and data-driven preclinical development (US).

Seven additional founding members are balanced across pharma, regulator experience, CRO, academia, the 3Rs and NAMs communities, microphysiological systems and organ-on-chip science, computational toxicology, digital technology, data standards, and translational science. Modality balance is an explicit recruitment criterion.

Regulators may participate in advisory or observer roles, consistent with agency rules. DiPS does not require regulators to serve as fiduciary board members.

Twelve to fifteen rotating members covering the foundation products and, later, funded working groups, with deliberate balance across animal-based, non-animal, and in-silico modalities. Two-year terms, one renewal permitted.

Parallel entities are being initiated by design. DiPS has begun the process of establishing a US membership-based nonprofit structure, anticipated as a 501(c)(6) organization, and a German eingetragener Verein (e.V.) based in Berlin. The US structure is intended to support engagement with FDA, NIH (including ORIVA), US industry, CROs, academia, and philanthropic partners. The German e.V. structure is intended to anchor DiPS in the European scientific and regulatory ecosystem, including proximity to BfR in Berlin and engagement with EMA and other EU bodies across Europe.

Cross-Atlantic presence is foundational, not optional.

Tiered membership: individual, academic, and corporate. Regulators participate free of charge where agency rules permit. Year-one founding partner participation is intentionally lighter than full working-group sponsorship. The first year proves the opportunity before asking partners for larger commitments.

Seven ways to join DiPS.

1. If you are a scientist working at the preclinical-to-clinical interface. Sign the charter and join the founding forum series. Your expertise will help define the discipline, the scope, the opportunity map, and the first foundational paper. Animal-based and non-animal scientists are equally welcome.

2. If you lead preclinical safety, toxicology, pharmacology, efficacy, or translational research at a pharma or biotech. Join the year-one founding partner circle. The first ask is not a large sponsorship. It is alignment around definition, value proposition, and the first credible outputs. After that, sponsor a precompetitive working group where the return on investment is clear.

3. If you work at a CRO, CDMO, vivarium technology organization, MPS provider, organoid platform company, in-silico software provider, data standards body, or digital platform company. Bring a use case, not a product pitch. DiPS welcomes technology providers when the starting point is a defined decision, evidence gap, evidence requirement, or study-design problem.

4. If you work at a regulatory agency. FDA, EMA, ICH, OECD, PMDA, NIH ORIVA, FDA ISTAND, or another public authority. Participate as a regulator-member or advisor where agency rules permit. Participation is free of charge. Participation does not imply endorsement. It means the work is shaped early enough to be scientifically and regulatorily legible.

5. If your organization leads in an adjacent domain. DIVA, Pistoia Alliance, 3Rs Collaborative, DiMe, FELASA, SOT, ESTIV, LASA, AAALAC, CDISC, ICCVAM, NICEATM, EURL ECVAM, ASPIS, ONTOX, EUROoCS, the IQ Consortium MPS Affiliate, CAAT, or another modality-specific, species-specific, data-specific, or discipline-specific society. We want a partnership pathway or memorandum of understanding, not a competition. The goal is digital continuity across existing silos, not another silo.

6. If you are none of the above but care about preclinical evidence reaching patients reliably. Attend the founding forum series. Broader meetings follow only when DiPS has a definition, opportunity map, and foundational product worth convening around.

7. If you want the discipline delivered to you weekly. Subscribe to The Digital Record. Our weekly newsletter tracks what DiPS published, what we partnered on, what we learned, and what we changed our minds about. Free to read. The fastest on-ramp for everyone else.

The success of DiPS will not be measured only by the members we recruit, the meetings we convene, or the papers we publish.

Its long-term measure is whether preclinical evidence becomes more reliable, more clinically predictive, more reproducible, more humane, and more useful for regulatory and portfolio decisions, regardless of which modality produced that evidence.

Because that outcome takes years to observe, DiPS will hold itself to three near-term lead measures.

1. Regulator-proximate outputs. The number of DiPS consensus outputs developed with documented participation, review, or advisory input from regulator or regulator-adjacent scientists at FDA, EMA, ICH, OECD, PMDA, NIH ORIVA, ICCVAM, NICEATM, EURL ECVAM, or other public authorities, where agency rules permit.

2. Sponsor adoption in active pipelines. The number of pharma, biotech, CRO, MPS provider, or technology partners that confidentially or publicly report formal use of a DiPS consensus output in an active preclinical program within twelve months of publication.

3. Federation coverage. The number of partnership pathways or memoranda with adjacent societies on both the in-vivo and the non-animal sides, each with documented non-overlap and, where appropriate, candidate co-branded outputs. This tracks whether DiPS is building digital continuity across silos or accidentally adding a new one.

If these three move, the long measure follows. If they do not, the long measure will not, and we will know early.

That is the standard.

We invite our members, our partners, and the regulators we work with to hold us to it.