Research & Development Program at GPC Regulatory

GPC Research & Development Programme 2026

Objectives, research streams, collaborations and results, 2019–2028

Global Product Compliance (Europe) AB – internal document, October 2026

1. Purpose and overall aim

GPC’s research and development programme exists to make chemical regulation and compliance more scientific, more efficient and less dependent on new testing, especially animal testing. It turns GPC’s practical experience as a regulatory partner and Lead Registrant into research questions, answers them with academic and industry partners, and builds the results into tools and services.

The programme is organised around five research objectives:

No.	Objective	Expected outcome
1	Regulatory science for compliance excellence and efficiency	Systematic knowledge of how regulations are built and depend on each other, turned into real-time, portfolio-specific compliance guidance (Intrims, RegAlert)
2	Avoidance of unnecessary testing	Criteria (DIM), read-across, QSAR and machine-learning models (Jadavpur University), and a methodology that show when new studies, especially animal studies, can be scientifically avoided
3	Group-based chemical safety assessment	Approaches for assessing groups of chemicals, with a reference set of about 150 chemical safety profiles as case examples
4	Data exchange and data mining	Platforms that find existing data, interpret it against regulatory requirements and make it available for reuse (Datagap, Chemsortia)
5	Cost-efficient and realistic biodegradation testing	Methods that cut the cost and time of degradation kinetics experiments by at least 50–60% and simulate real, complex effluents

The objectives build on each other: regulatory science (1) defines what data are needed; testing avoidance (2) and group assessment (3) show how needs can be met without new studies; the data platforms (4) find and share the data that already exist; and better biodegradation methods (5) make the studies that are still needed cheaper and more relevant.

2. GPC R&D at a glance

Item	Summary
Base	Ideon Science Park, Lund (Beta 5, Scheelevägen 17)
Core R&D team	Shisher Kumra, Dr Anders Bergqvist, Dr Zsuzsanna Szepesi, Dr Vaibhav Diwan, Mirach Pelster
Main partners	DTU (Denmark), IIT Bombay, Lund University (incl. LTH), Jadavpur University, the Animal-Free Safety Assessment (AFSA) Collaboration, KTH, RISE, S&P Global
Funding	GPC’s own resources; co-financing of university projects (DTU); Vinnova-funded project with GPC in kind
Publications	Including four papers with Jadavpur University on read-across, QSAR and machine-learning models; peer-reviewed papers in Regulatory Toxicology and Pharmacology, Chemical Research in Toxicology, SAR and QSAR in Environmental Research and Aquatic Toxicology; further manuscripts and a book chapter in progress
Intellectual property	Indian patent application 202521067103 (Intrims technology), filed July 2025, published September 2026
Products from research	Intrims (launch 2027), RegAlert, Datagap (launch December 2026), Chemsortia (launch May 2027)
Training	More than 20 interns and thesis students since 2019, mainly from Lund University

3. People and roles

사람	Role	Objectives
Shisher Kumra	Leads the R&D programme and the regulatory science research; concept and inventor of Intrims; concept for Datagap and Chemsortia	1, 4 (and PFAS within 1)
Dr Anders Bergqvist	Head of toxicology (PhD Immunology, Lund University); leads the chemical safety profiles and the Handbook	2, 3
Dr Zsuzsanna Szepesi	Researcher, in silico toxicology (PhD Immunology, Lund University); read-across, QSAR and machine-learning models with Jadavpur University; chemical safety profiles	2, 3
Dr Vaibhav Diwan	Internship and DTU Research Manager (PhD, DTU 2019); also Business Development & Growth	1, 4, 5
Mirach Pelster	Researcher and developer (chemical engineer, MSc)	1, 4

Regulatory dossiers and other client deliverables are prepared by GPC’s regulatory staff, so that R&D time and client work can be kept apart.

4. Objective 1 – Regulatory science for compliance excellence and efficiency

Why it matters. Chemicals regulations worldwide differ in structure, definitions and requirements, and companies struggle to know what they must do, in which order, for each substance and market. Systematic analysis of regulations makes compliance faster, cheaper and more reliable, and helps countries design better regulation.

Research questions. How are chemicals regulations structured, and how do their requirements depend on each other? Can best practice be identified and adapted to different countries? Can a country’s chemicals inventory be predicted from its industrial structure? How can this knowledge be turned into real-time, company-specific guidance?

Activities and results

Activity	Period	Key result
Chemicals inventory estimation model	2022 – ongoing	Predicts a country’s inventory from its industry structure (manufacturing-, application- or consumer-intensive) and economic level; tested on three countries with 75–80% correct results; online tool in the Global Inventory Report (January 2025)
Global regulatory framework and best practice	2022 – 2026	Regulations evaluated against about 18 criteria; best practice per criterion; overall model adaptable to countries; Global Chemical Regulatory Framework (November 2026)
Survey on the impact of chemical safety regulation	2025	170 industry respondents; most see a lack of regulation as harmful to business and to a level playing field (Asia Chemicals Forum 2025)
Analysis of regulatory selection of substances for evaluation	2024 – July 2025	How selection for evaluation compares with substances of priority concern
PFAS identification and regulatory implications	2025 – March 2027	Computational method to identify PFAS and their sub-groups and link them to legal implications; thesis, white paper with S&P Global, IPI article
Regulatory barriers to continuous manufacturing of biologics (Vinnova, Lund University)	2024 – 2025	GPC as regulatory partner; manuscript submitted (2026); EUFEPS presentation

From research to products

Intrims (2024 – launch 2027): standardises the requirements of all applicable regulations into one set of compliance actions, groups them into about 20 bundles by interdependency (OR, AND, MIXED, SINGLE logics), sequences the bundles and applies them to a company’s own portfolio, with automatic re-evaluation. Tested with more than 40 users (2024) and more than 25 users (2025); upgraded after testing. Patent application 202521067103 (India).

RegAlert: real-time regulatory alerts based on the same knowledge base.

Data foundation. Annual GPC reviews of chemicals regulations: Global Regulatory Guide, GHS Compliance guide, Global Inventory Report, Annual Summary, Compendium of Global Chemical Regulation, and the PFAS regulation overview.

5. Objective 2 – Avoidance of unnecessary testing

Why it matters. Many studies requested under REACH, including animal studies, are difficult, impossible or meaningless for certain substances, or could be replaced by existing data and non-animal methods.

Research questions. When is an animal test difficult, impossible or meaningless? Which read-across, QSAR and machine-learning models can reliably replace specific studies? How can a Lead Registrant decide, case by case, whether a requested study can be scientifically avoided?

Activities and results

Activity	Partner	Period	Key result
DIM criteria for difficult, impossible and meaningless animal testing	AFSA Collaboration	Feb 2025 – Aug 2026; next phase to Q3 2027	Criteria applied to 1,740 substances; 35 avoidable fish studies, about 5,000–10,000 animals; van Velthoven et al. (2026)
Read-across, QSAR and machine-learning models (details below)	Jadavpur University	2023 – 2026, continuing	15 peer-reviewed publications from the collaboration, 4 with GPC co-authors; micronucleus model submitted (2026)
Testing-avoidance methodology as Lead Registrant	Internal	2022 – ongoing	Combines existing data, read-across, models and DIM criteria; study requirements reduced in about 10–15% of the substances assessed in 2025–2026
Earlier work: REACH last-resort requirement; difficult and coloured substances	AFSA; DTU	2019 – 2024	Macmillan et al. (2024); DTU project 2019–2021

In silico and read-across methods with Jadavpur University

Together with the Drug Theoretics and Cheminformatics Laboratory at Jadavpur University (Prof. Kunal Roy), GPC develops computational methods that can replace or reduce animal studies: read-across, QSAR, machine-learning models and combined approaches such as q-RASAR (quantitative read-across structure-activity relationships). GPC formulates the research questions from its experience of animal studies and of gaps in existing models, compiles training data from its own and public sources, defines the regulatory endpoints and applicability needs, and helps adapt the final models. The collaboration, running in two projects since 2023, has produced 15 peer-reviewed publications (full list in section 14), of which those below have GPC co-authors.

Endpoint (OECD test guideline)	Approach	Publication	Status
Bioconcentration in fish (TG 305)	Machine-learning q-RASAR	Pore, Pelloux et al. (2024), Journal of Hazardous Materials 479, 135725	Published
Fish early-life-stage toxicity (TG 210)	Intelligent consensus prediction (combined QSAR models)	Pore, Pelloux, Bergqvist et al. (2025), Aquatic Toxicology 279, 107216	Published
Reproductive and developmental toxicity (TG 421/422)	Read-across-driven classification	Chatterjee, Pore, Szepesi and Roy (2025), SAR and QSAR in Environmental Research 36(3), 247–270	Published
Repeated-dose toxicity (TG 407, 408, 422)	Machine-learning QSAR, with experimental validation	Pore, Szepesi and Roy (2026), Chemical Research in Toxicology 39(4), 544–565	Published
In vivo micronucleus (TG 474)	Integrated machine learning (structural, metabolic and experimental information)	Das, Szepesi and Roy, Toxicology in Vitro	Submitted October 2026
In vivo comet assay (TG 489)	In silico modelling	Das, Szepesi and Roy	In preparation
NAMs in genetic toxicology	Review of regulatory screening and NAM application	Das, Szepesi, Ojha and Roy, book chapter, Comprehensive Computational Toxicology (Elsevier)	Due 30 November 2026

GPC authors in bold. The models cover endpoints for which animal studies are normally required under REACH, and they feed directly into the testing-avoidance methodology (below) and the chemical safety profiles (Objective 3).

Principle. As Lead Registrant for more than 200 substances, GPC could simply commission the studies ECHA requests. It chooses to research whether they can be avoided, accepting a smaller commercial gain from study management.

6. Objective 3 – Group-based chemical safety assessment (new stream)

Why it matters. Assessing chemicals one by one is slow and drives repeated testing. Regulators increasingly expect grouping and read-across, but there are few well-documented reference cases showing how group assessment can be done robustly.

Research questions. How can chemicals be grouped by functional group and chemistry category so that data and conclusions can be shared within a group? What evidence makes a group assessment scientifically defensible?

Activities

Handbook of Chemical Safety Profiles: a reference set of detailed, endpoint-by-endpoint safety profiles for about 150 chemicals, by December 2028. More than 30 profiles completed in 2025–2026. Each profile records the reliability of the data, the conclusion per endpoint, the remaining data gaps and any scientific basis for avoiding studies. Lead: Dr Anders Bergqvist and Dr Zsuzsanna Szepesi.

Group assessment approaches: functional-group and chemistry-category toxicological profiles developed from the Handbook, with the profiles serving as case examples for group chemical safety assessment.

Expected outcome. A documented basis for group assessments that reduces the need for future studies across whole groups of substances.

7. Objective 4 – Data exchange and data mining

Why it matters. Relevant data often exist but are scattered across databases, literature and private data owners, so studies are repeated unnecessarily.

Activities and results

Platform	Period	What it does	Results
Datagap (data mining)	May 2026 – launch Dec 2026; internal use since Jul 2026	Searches more than 150 sources (databases, literature, data owners, handbooks, government publications), presents data by required endpoint per region, and identifies data gaps and studies needed per submission	Platform in internal use
Chemsortia (data exchange and sale)	Mid-2024 – launch May 2027	Centralises information on existing study data and matches it to companies’ requirements; covers data GPC holds, data with other owners, and new study design where no data exist	Data on more than 10 substances shared, avoiding 25–80% of new studies for those substances

The platforms use the regulatory science of Objective 1 (what is required) and the methods of Objectives 2 and 3 (when existing data are enough).

8. Objective 5 – Cost-efficient and realistic biodegradation testing

Why it matters. Biodegradation kinetics experiments are expensive and slow, and standard tests use single substances at high concentrations, unlike real effluents where many chemicals degrade together at low concentrations.

Aims.

Minimise the cost of studies on degradation kinetics by testing many chemicals together in one experiment.

Simulate real situations by testing complex effluents and discharge samples.

Cut the cost and time of degradation kinetics experiments by at least 50–60% compared with testing substances individually.

Activities and results

Activity	Partner	Period	Key result
Biodegradation kinetics of chemicals in mixtures (co-financed research)	DTU	2019 – 2022	GPC defined the research question (heterogeneous mixtures of industrial chemicals); Birch et al. (2023), Biodegradation and MethodsX; one paper discussed at ECHA and the EU PBT expert group
Biodegradation of large numbers of discharged chemicals (PhD project)	DTU, IIT Bombay	15 Feb 2025 – Q3 2028	GPC proposed extending the method from GC-MS to LC-MS, so that higher-melting substances can be included; laboratory work at DTU (from Oct 2025) and in river water at IIT Bombay (from Feb 2026)

[Add how the 50–60% saving will be measured, e.g. cost and duration per substance in mixture testing versus individual testing.]

9. Emerging area: sustainability and ESG

Intern projects in 2024–2026 analysed more than 1,000 sustainability (BRSR) reports and reviewed climate regulation and ESG reporting, enabling GPC to offer Sustainability Services. The next step is to turn the data analysis into a documented research output.

10. Collaboration model

Partner	Type	Period	Objective
DTU	Co-financed research; co-funded PhD	2019–2022; 2025–2028	5
IIT Bombay	PhD partner	2025–2028	5
Jadavpur University	Joint modelling research	2023–2026	2, 3
AFSA Collaboration	Industry–NGO research consortium	2023–2027	2
Lund University / LTH	Joint research, theses, internships	2019–2026	1, 4
KTH, RISE, industry partners	Vinnova project	2024–2025	1
S&P Global	Joint PFAS white paper	2026	1

Students linked to R&D (2025–2026): PFAS intern and thesis student (LTH); software and AI interns supporting the platforms.

11. How we work

Identify a gap from practice: a test that gives no meaningful result, a missing model, a regulation that is hard to apply.

Formulate the research question and how the answer will be used.

Choose partners and agree roles, budget and publication rights in writing.

Plan with milestones, and keep project files (agreements, minutes, drafts, data).

Do the research, reviewing results continuously.

Publish in peer-reviewed journals, reports and conferences.

Transfer results into methods, platforms and services.

12. Timeline

Year	Milestones
2019	DTU mixture biodegradation project and DTU difficult-substances project start
2022	DTU mixture project concludes; regulatory science research and testing-avoidance methodology start
2023	Jadavpur collaboration on read-across and QSAR models starts; DTU mixture papers
2024	Intrims development and first user tests (40+ users); Chemsortia development starts; Macmillan et al. (AFSA); Vinnova biologics project
2025	DTU PhD starts (Feb); DIM project; Intrims patent filed (Jul); second Intrims user tests (25+ users); Global Inventory Report; Asia Chemicals Forum survey; Jadavpur papers
2026	DIM paper (Aug); repeated-dose toxicity paper; Datagap (May–Dec); PFAS thesis and white paper; biologics manuscript; patent published (Sep); Global Chemical Regulatory Framework (Nov)
2027	Intrims and Chemsortia launch; DIM next phase; PFAS method completed
2028	DTU PhD concludes; Handbook of about 150 chemical safety profiles completed

13. Managing and documenting R&D

Monthly time recording per person, by project and by R&D versus other work.
One project register with research question, objective, partners, dates, staff and status.
Project files with agreements, plans, minutes, correspondence, drafts, data and results.
Development records for Intrims, Datagap and Chemsortia: versions, test rounds and change logs.
Publication and acknowledgement policy agreed with partners at the start.
Annual R&D report summarising objectives, projects, outputs and time spent.