Autosomal Dominant Polycystic Kidney Disease (ADPKD)SAILING-ADPKDNCT07016282
Simulated and Synthetic Health Data: Improving Clinical Research on Rare Diseases. A Real-World Data Simulation of Autosomal Dominant Polycystic Kidney Disease (ADPKD) Trials. A Retrospective, Observational Study
An observational study of Autosomal Dominant Polycystic Kidney Disease (ADPKD), run by Mario Negri Institute for Pharmacological Research at 2 sites in 2 countries.
Trial summary
This is a no-profit, retrospective observational study involving real-world data (RWD), retrieved from ADPKD-related electronic health records stored at Mario Negri Institute IRCCS. RWD will be used to generate simulated and synthetic datasets, using AI tools. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Autosomal Dominant Polycystic Kidney Disease (ADPKD)
- How many people
- 100 participants
- Where
- 2 sitesin 2 countries
- Study dates
- 12 Jun 2025to Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Data from ADPKD patients collected in the form of electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on two completed studies performed at the Mario Negri Institue i.e., the ALADIN Study (NCT00309283) and the ALADIN 2 Study (NCT01377246), which enrolled ADPKD-patients treated either with Octreotide-LAR or placebo.
Who may be able to join
- Adult (>18 years) men and women with ADPKD according to Ravine criteria25
- Estimated glomerular filtration rate (eGFR) between 15 and 40 mL/min/1.73 m2 (CKD stage: G3b-G4) or higher (CKD stage: G1-G3a), as calculated by the Modification of Diet in Renal Disease study four variables equation
Who cannot join
- confounding factors that could affect renal function loss independent of kidney growth and treatment allocation (i.e., diabetes mellitus, urinary protein excretion rate >3 g/24 h)
- Abnormal urinalysis suggestive of concomitant, clinically significant glomerular disease, and urinary tract lithiasis or infection
- Patients with major systemic disease
- Patients unable to provide informed consent
- Pregnant, lactating, or potentially childbearing women without adequate contraception
Trial locations and contacts
2 sites in 2 countries.
2 sites in 2 countries
Italy1 site
Clinical Research Centre for Rare Diseases Aldo e Cele Daccò
Ranica, BG, 24020
Sweden1 site
Department of Global Public Health (GPH), Karolinska Institutet
Stockholm, 171 77
About this study
This is a no-profit, retrospective observational study involving real-world data (RWD), retrieved from ADPKD-related electronic health records stored at Mario Negri Institute IRCCS. RWD will be used to generate simulated and synthetic datasets, using AI tools. RWD and generated data (GD) will be used to conduct three virtual RCTs, which main outcome is change in Total Kidney Volume (TKV). Statistical tests will be performed to assess quality and privacy preservation of GD compared with RWD. GD will be also evaluated in exploratory sample size estimations.
Read the detailed description
Randomized clinical trials (RCTs) can be regarded as the least biased source of information to address intervention questions. One of the most common problems encountered in clinical trials focused on rare diseases is the difficulty in finding patients and therefore in building trials on sufficiently large population, in order to have more robust data and less methodological distortions. Several stratagems are already in use to deal with these problems, including extended trial duration, repeated outcome measures, patients genetic profiles, surrogate endpoint, multicenter studies. Another approach is to consider other trial designs in addition to parallel-arms design, such as crossover trial, n-of-1 trials, and adaptive trials.
Simulated and synthetic health data can represent new valid approaches to increase the representativeness of the patients, especially in rare diseases field, while reducing costs and time constraints, but also facing the limitations imposed by national and international regulations concerning privacy and data management. Simulation studies are defined as computer experiments that involve creating data by pseudo-random sampling from known probability distributions, based on Monte Carlo method. A promising approach now under development includes synthetic data, defined as artificially generated data with the aim of reproducing the statistical properties of an original dataset, through generative large languages models (LLMs).
Thus, while simulated data rely on known distributions that must be specified in advance, synthetic data are generated by LLMs that learn these distributions from training data, without the need for predefined distributions, offering a significant advantage in flexibility and applicability.
This study aims to find the most suitable tool for generating simulated and synthetic data in rare diseases field, and to compare the fidelity, quality, and privacy preservation of these datasets, derived from real-world ADPKD clinical trial data. Furthermore, a virtual clinical trial will be conducted using these three datasets to assess their validity in replicating real trial outcomes.
Finally, retrieved and generated data will be used to assess new sample size estimations for future clinical trial performed at the Clinical Research Center for Rare Disease "Aldo e Cele Daccò", Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica (BG), Italy.
By using generative AI models, such as Generative Adversarial Networks (GANs), this study aims to overcome challenges related to data poverty and trial design. The results could provide valuable insights into whether synthetic data can be a useful tool for improving clinical trials in rare diseases, making them more efficient and cost-effective.
- Keywords
- rare diseases, RCTs, Monte Carlo simulation, generative AI, ADPKD
- Medical subject headings
- Polycystic Kidney, Autosomal Dominant, Rare Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Real-world data form ADPKD patients | Real-world data from ADPKD-related electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on the ALADIN (NCT00309283) and ALADIN 2 (NCT01377246) studies | |
| Simulated data | Data based on RWD from the ADPKD patients and derived from predefined statistical models (e.g., normal distribution for continuous variables, binomial distribution for categorical variables). | |
| Synthetic data | Data generated from the RWD of the ADPKD patients using generative large languages models (LLMs) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Changes in total kidney volume (TKV)
Changes in TKV in mL.
Measured: At baseline, and immediately after data generation procedure.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2025
- Study start12 Jun 2025
- Primary completionJun 2027 (estimated)
- Study completionJun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (27)Papers the sponsor has linked to this study.
- Other trials for Autosomal Dominant Polycystic Kidney Disease (ADPKD)Studies of the same condition, open and under way.
- More trials run by Mario Negri Institute for Pharmacological ResearchThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Giuseppe Remuzzi, M.D., Study director, Istituto Di Ricerche Farmacologiche Mario Negri
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07016282
- Sponsor's protocol ID
- SAILING-ADPKD
- Acronym
- SAILING-ADPKD
- Status last verified
- Aug 2025
- First submitted
- 27 May 2025
- Last updated
- 2 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Bolignano D, Pisano A. Good-quality research in rare diseases: trials and tribulations. Pediatr Nephrol. 2016 Nov;31(11):2017-23. doi: 10.1007/s00467-016-3323-7. Epub 2016 Jan 27.
PubMed 26817476Halpern SD, Karlawish JH, Berlin JA. The continuing unethical conduct of underpowered clinical trials. JAMA. 2002 Jul 17;288(3):358-62. doi: 10.1001/jama.288.3.358.
PubMed 12117401Lilford RJ, Thornton JG, Braunholtz D. Clinical trials and rare diseases: a way out of a conundrum. BMJ. 1995 Dec 16;311(7020):1621-5. doi: 10.1136/bmj.311.7020.1621.
PubMed 8555809Gagne JJ, Thompson L, O'Keefe K, Kesselheim AS. Innovative research methods for studying treatments for rare diseases: methodological review. BMJ. 2014 Nov 24;349:g6802. doi: 10.1136/bmj.g6802.
PubMed 25422272Shurin S, Krischer J, Groft SC. Clinical trials In BMT: ensuring that rare diseases and rarer therapies are well done. Biol Blood Marrow Transplant. 2012 Jan;18(1 Suppl):S8-11. doi: 10.1016/j.bbmt.2011.10.030. No abstract available.
PubMed 22226117van der Lee JH, Wesseling J, Tanck MW, Offringa M. Efficient ways exist to obtain the optimal sample size in clinical trials in rare diseases. J Clin Epidemiol. 2008 Apr;61(4):324-30. doi: 10.1016/j.jclinepi.2007.07.008. Epub 2008 Feb 21.
PubMed 18313556Stone EM. Challenges in genetic testing for clinical trials of inherited and orphan retinal diseases. Retina. 2005 Dec;25(8 Suppl):S72-S73. doi: 10.1097/00006982-200512001-00034. No abstract available.
PubMed 16374347Buckley BM. Clinical trials of orphan medicines. Lancet. 2008 Jun 14;371(9629):2051-5. doi: 10.1016/S0140-6736(08)60876-4. No abstract available.
PubMed 18555919Kinder B, McCormack FX. Clinical trials for rare lung diseases: lessons from lymphangioleiomyomatosis. Lymphat Res Biol. 2010 Mar;8(1):71-9. doi: 10.1089/lrb.2009.0027.
PubMed 20235889Lagakos SW. Clinical trials and rare diseases. N Engl J Med. 2003 Jun 12;348(24):2455-6. doi: 10.1056/NEJMe030024. No abstract available.
PubMed 12802033Berlin JA. N-of-1 clinical trials should be incorporated into clinical practice. J Clin Epidemiol. 2010 Dec;63(12):1283-4. doi: 10.1016/j.jclinepi.2010.05.006. Epub 2010 Aug 30.
PubMed 20800449Cerqueira FP, Jesus AMC, Cotrim MD. Adaptive Design: A Review of the Technical, Statistical, and Regulatory Aspects of Implementation in a Clinical Trial. Ther Innov Regul Sci. 2020 Jan;54(1):246-258. doi: 10.1007/s43441-019-00052-y. Epub 2020 Jan 6.
PubMed 32008232Yoon J, Drumright LN, van der Schaar M. Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE J Biomed Health Inform. 2020 Aug;24(8):2378-2388. doi: 10.1109/JBHI.2020.2980262. Epub 2020 Mar 12.
PubMed 32167919Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019 May 20;38(11):2074-2102. doi: 10.1002/sim.8086. Epub 2019 Jan 16.
PubMed 30652356METROPOLIS N, ULAM S. The Monte Carlo method. J Am Stat Assoc. 1949 Sep;44(247):335-41. doi: 10.1080/01621459.1949.10483310. No abstract available.
PubMed 18139350
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Simulated and Synthetic Health Data: Improving Clinical Research on Rare Diseases. A Real-World Data Simulation of Autosomal Dominant Polycystic Kidney Disease (ADPKD) Trials. A Retrospective, Observational Study
- ClinicalTrials.gov ID
- NCT07016282
- Acronym / sponsor ID
- SAILING-ADPKD / SAILING-ADPKD
- Keywords
- rare diseases, RCTs, Monte Carlo simulation, generative AI, ADPKD
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Mario Negri Institute for Pharmacological ResearchUniversity, hospital or other
- Study start
- 12 Jun 2025 (actual)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Jun 2027 (estimated)
- First posted
- 11 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This is a no-profit, retrospective observational study involving real-world data (RWD), retrieved from ADPKD-related electronic health records stored at Mario Negri Institute IRCCS. RWD will be used to generate simulated and synthetic datasets, using AI tools. RWD and generated data (GD) will be used to conduct three virtual RCTs, which main outcome is change in Total Kidney Volume (TKV). Statistical tests will be performed to assess quality and privacy preservation of GD compared with RWD. GD will be also evaluated in exploratory sample size estimations.
Detailed description
Randomized clinical trials (RCTs) can be regarded as the least biased source of information to address intervention questions. One of the most common problems encountered in clinical trials focused on rare diseases is the difficulty in finding patients and therefore in building trials on sufficiently large population, in order to have more robust data and less methodological distortions. Several stratagems are already in use to deal with these problems, including extended trial duration, repeated outcome measures, patients genetic profiles, surrogate endpoint, multicenter studies. Another approach is to consider other trial designs in addition to parallel-arms design, such as crossover trial, n-of-1 trials, and adaptive trials.
Simulated and synthetic health data can represent new valid approaches to increase the representativeness of the patients, especially in rare diseases field, while reducing costs and time constraints, but also facing the limitations imposed by national and international regulations concerning privacy and data management. Simulation studies are defined as computer experiments that involve creating data by pseudo-random sampling from known probability distributions, based on Monte Carlo method. A promising approach now under development includes synthetic data, defined as artificially generated data with the aim of reproducing the statistical properties of an original dataset, through generative large languages models (LLMs).
Thus, while simulated data rely on known distributions that must be specified in advance, synthetic data are generated by LLMs that learn these distributions from training data, without the need for predefined distributions, offering a significant advantage in flexibility and applicability.
This study aims to find the most suitable tool for generating simulated and synthetic data in rare diseases field, and to compare the fidelity, quality, and privacy preservation of these datasets, derived from real-world ADPKD clinical trial data. Furthermore, a virtual clinical trial will be conducted using these three datasets to assess their validity in replicating real trial outcomes.
Finally, retrieved and generated data will be used to assess new sample size estimations for future clinical trial performed at the Clinical Research Center for Rare Disease "Aldo e Cele Daccò", Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica (BG), Italy.
By using generative AI models, such as Generative Adversarial Networks (GANs), this study aims to overcome challenges related to data poverty and trial design. The results could provide valuable insights into whether synthetic data can be a useful tool for improving clinical trials in rare diseases, making them more efficient and cost-effective.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Real-world data form ADPKD patients | – | –Real-world data from ADPKD-related electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on the ALADIN (NCT00309283) and ALADIN 2 (NCT01377246) studies |
| Simulated data | – | –Data based on RWD from the ADPKD patients and derived from predefined statistical models (e.g., normal distribution for continuous variables, binomial distribution for categorical variables). |
| Synthetic data | – | –Data generated from the RWD of the ADPKD patients using generative large languages models (LLMs) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Data from ADPKD patients collected in the form of electronic health records (EHR) stored at the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, primarily based on two completed studies performed at the Mario Negri Institue i.e., the ALADIN Study (NCT00309283) and the ALADIN 2 Study (NCT01377246), which enrolled ADPKD-patients treated either with Octreotide-LAR or placebo.
Key inclusion criteria (2 listed)
- Adult (>18 years) men and women with ADPKD according to Ravine criteria25
- Estimated glomerular filtration rate (eGFR) between 15 and 40 mL/min/1.73 m2 (CKD stage: G3b-G4) or higher (CKD stage: G1-G3a), as calculated by the Modification of Diet in Renal Disease study four variables equation
Key exclusion criteria (5 listed)
- confounding factors that could affect renal function loss independent of kidney growth and treatment allocation (i.e., diabetes mellitus, urinary protein excretion rate >3 g/24 h)
- Abnormal urinalysis suggestive of concomitant, clinically significant glomerular disease, and urinary tract lithiasis or infection
- Patients with major systemic disease
- Patients unable to provide informed consent
- Pregnant, lactating, or potentially childbearing women without adequate contraception
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Changes in total kidney volume (TKV)
Time frame: At baseline, and immediately after data generation procedure.
Changes in TKV in mL.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2025.
- Background: PubMed 26817476Bolignano D, Pisano A. Good-quality research in rare diseases: trials and tribulations. Pediatr Nephrol. 2016 Nov;31(11):2017-23. doi: 10.1007/s00467-016-3323-7. Epub 2016 Jan 27.
- Background: PubMed 12117401Halpern SD, Karlawish JH, Berlin JA. The continuing unethical conduct of underpowered clinical trials. JAMA. 2002 Jul 17;288(3):358-62. doi: 10.1001/jama.288.3.358.
- Background: PubMed 8555809Lilford RJ, Thornton JG, Braunholtz D. Clinical trials and rare diseases: a way out of a conundrum. BMJ. 1995 Dec 16;311(7020):1621-5. doi: 10.1136/bmj.311.7020.1621.
- Background: PubMed 25422272Gagne JJ, Thompson L, O'Keefe K, Kesselheim AS. Innovative research methods for studying treatments for rare diseases: methodological review. BMJ. 2014 Nov 24;349:g6802. doi: 10.1136/bmj.g6802.
- Background: PubMed 22226117Shurin S, Krischer J, Groft SC. Clinical trials In BMT: ensuring that rare diseases and rarer therapies are well done. Biol Blood Marrow Transplant. 2012 Jan;18(1 Suppl):S8-11. doi: 10.1016/j.bbmt.2011.10.030. No abstract available.
- Background: PubMed 18313556van der Lee JH, Wesseling J, Tanck MW, Offringa M. Efficient ways exist to obtain the optimal sample size in clinical trials in rare diseases. J Clin Epidemiol. 2008 Apr;61(4):324-30. doi: 10.1016/j.jclinepi.2007.07.008. Epub 2008 Feb 21.
- Background: PubMed 16374347Stone EM. Challenges in genetic testing for clinical trials of inherited and orphan retinal diseases. Retina. 2005 Dec;25(8 Suppl):S72-S73. doi: 10.1097/00006982-200512001-00034. No abstract available.
- Background: PubMed 18555919Buckley BM. Clinical trials of orphan medicines. Lancet. 2008 Jun 14;371(9629):2051-5. doi: 10.1016/S0140-6736(08)60876-4. No abstract available.
- Background: PubMed 20235889Kinder B, McCormack FX. Clinical trials for rare lung diseases: lessons from lymphangioleiomyomatosis. Lymphat Res Biol. 2010 Mar;8(1):71-9. doi: 10.1089/lrb.2009.0027.
- Background: PubMed 12802033Lagakos SW. Clinical trials and rare diseases. N Engl J Med. 2003 Jun 12;348(24):2455-6. doi: 10.1056/NEJMe030024. No abstract available.
- Background: PubMed 20800449Berlin JA. N-of-1 clinical trials should be incorporated into clinical practice. J Clin Epidemiol. 2010 Dec;63(12):1283-4. doi: 10.1016/j.jclinepi.2010.05.006. Epub 2010 Aug 30.
- Background: PubMed 32008232Cerqueira FP, Jesus AMC, Cotrim MD. Adaptive Design: A Review of the Technical, Statistical, and Regulatory Aspects of Implementation in a Clinical Trial. Ther Innov Regul Sci. 2020 Jan;54(1):246-258. doi: 10.1007/s43441-019-00052-y. Epub 2020 Jan 6.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Giuseppe Remuzzi, M.D. study director
Istituto Di Ricerche Farmacologiche Mario Negri
2 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Autosomal Dominant Polycystic Kidney Disease (ADPKD), run by Mario Negri Institute for Pharmacological Research at 2 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 2 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Italy, Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in total kidney volume (TKV) (time frame: At baseline, and immediately after data generation procedure.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07016282, which also lists 27 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2025.