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The process of developing novel therapeutics is increasingly slow and costly1, with an average of US$2–3 billion spent over the course of 10–15 years to bring a new drug to market2. Advances in artificial intelligence (AI) have demonstrated proof-of-concept innovation and potential to accelerate and decrease costs across all stages of pharmaceutical research and development by facilitating disease-associated target prioritization3,4,5, compound design and optimization6,7,8,9,10, and clinical trial performance11,12,13,14,15. Shifting more of the time-, material- and labor-intensive steps of drug discovery to AI platforms enables more rapid nomination of therapeutic candidate compounds that explore the possible chemical space more thoroughly16,17 and with a concomitant reduction in hands-on screening18,19, thus allowing more preclinical candidate compounds to enter clinical testing and, eventually, clinical practice.

Despite these advancements, few AI-discovered or AI-designed drugs have reached clinical trials. AI-discovered drugs have experienced similar levels of phase 2 trial failure as non-AI-discovered drugs20,21, and none has so far progressed through phase 3 trials21. The question of whether AI can impart meaningful, sustained disruption to drug development has remained unanswered21.

As an answer to this question, our group used generative AI-driven discovery tools22,23 to identify Traf2- and Nck-interacting kinase (TNIK) de novo as a critical regulator of idiopathic pulmonary fibrosis (IPF) pathology as it orchestrates multiple profibrotic and proinflammatory cellular programs9. Our approach resulted in the first antifibrotic, small-molecule TNIK inhibitor, rentosertib (formerly ISM001-055), for the treatment of IPF designed by generative AI. We reported the development and positive phase 0 and 1 trial results of rentosertib9 (NCT05154240 and CTR20221542 (accessed via http://www.chinadrugtrials.org.cn/)), the first report of a targeted TNIK inhibitor entering clinical testing, which found rentosertib to be safe and well tolerated by healthy individuals, exhibiting a favorable pharmacokinetic (PK) profile in both trials9. This was also, importantly, the first reported instance of AI platform-enabled discovery of both a disease-associated target and a compound for that target. Our generative AI-powered approach streamlined preclinical candidate nomination to a mere 18 months and completion of phase 0/1 clinical testing to under 30 months from the initiation of target discovery, representing a revolutionary shift in the streamlining of drug discovery9.

IPF is an age-related, progressive lung disease diagnosed by excluding other potential causes of interstitial lung disease (for example, environmental exposures, connective tissue disease, hypersensitivity pneumonitis or drug toxicities)24 and marked by interstitial pneumonia, dyspnea and cough25, histologically characterized by fibroblast proliferation and extracellular matrix remodeling26,27,28,29. Affecting between 10 and 60 per 100,000 individuals in the USA, with an approximately tenfold greater incidence in those over 65, IPF occurs at a rate similar to stomach, brain and testicular cancers25. The median survival time after diagnosis is 2–4 years despite widespread availability of targeted antifibrotic therapies26,30, highlighting the ongoing need for novel, effective therapies to treat IPF. The current standard-of-care (SOC) therapy regimen to treat IPF includes either nintedanib, a broadly acting inhibitor of receptor tyrosine kinases31, or pirfenidone, an inhibitor of TGFβ-mediated fibroblast-to-myofibroblast transition32. Clinical trials investigating these two drugs in patients with IPF have demonstrated only a slowing of disease progression33,34, as measured by forced vital capacity (FVC) and self-reported quality of life (QOL) metrics, and unclear benefit to survival26, making the development of therapies that can restore lung function and reverse the course of disease a priority.

Here, we present the results of a phase 2a multicenter, double-blind, randomized, placebo-controlled trial to assess the safety, tolerability, PK and impact on FVC of rentosertib at a range of doses for up to 12 weeks in patients with IPF. Additional secondary and exploratory endpoints and analyses were designed to further measure lung function, QOL metrics and cellular phenotypes associated with rentosertib treatment and response.

Results

Patients

Among the 128 patients screened for inclusion, 71 (55.5%) patients were enrolled and randomly assigned to receive placebo (n = 17), 30 mg rentosertib once daily (QD, n = 18), 30 mg rentosertib twice daily (BID, n = 18) or 60 mg rentosertib QD (n = 18) (Extended Data Fig. 1). The most common reason for screening-based exclusion was obstructive pulmonary disease comorbidity indicated by forced expiry in 1 s (FEV1)/FVC less than 0.7 at visit 1 (baseline) or diffusion capacity of the lung for carbon monoxide (DLCO) percent of predicted normal falling outside the range of ≥25% and <80% at baseline. Patients were also excluded for a recent history of upper or lower respiratory tract infection (within 4 weeks) at visit 1 (baseline) or before visit 2 (week 1). The intention-to-treat population included 71 patients, and 55 (77%) completed the 12-week placebo-controlled period (15 (88%) in the placebo group, 16 (89%) in the 30 mg rentosertib QD group, 12 (67%) in the 30 mg rentosertib BID group and 12 (67%) in the 60 mg rentosertib QD group). Overall, 16 (22.5%) patients discontinued treatment before the conclusion of the 12-week study due to either adverse events (AEs) or other withdrawal; 2 from placebo, 2 from 30 mg rentosertib QD, 6 from 30 mg rentosertib BID and 6 from 60 mg rentosertib QD groups. All patients were included in the safety and efficacy analyses (Fig. 1).

Fig. 1: Trial participant randomization and follow-up scheme.
figure 1

Of 128 patients screened for inclusion, 71 were randomized to receive 30 mg rentosertib QD (n = 18), 30 mg rentosertib BID (n = 18), 60 mg rentosertib QD (n = 18), or placebo (n = 17) over the course of 12 weeks. Sixteen patients discontinued treatment prior to the end of treatment.

Source data

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Clinical and demographic characteristics, including age, body mass index (BMI) and baseline lung function, were similar across treatment groups (Table 1).

Table 1 Characteristics of the patients at baseline
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Primary safety endpoint

The rates of treatment-emergent AEs (TEAEs) were similar across all treatment groups (13/18 (72.2%) in the 30 mg rentosertib QD group, 15/18 (83.3%) in the 30 mg rentosertib BID group, 15/18 (83.3%) in the 60 mg rentosertib QD group and 12/17 (70.6%) in the placebo group) (Table 2), with similar incidence for patients concurrently taking SOC antifibrotic therapy or not (Supplementary Table 1). Treatment-related AEs were more common among patients who received rentosertib compared with placebo, with 5/17 (29.4%) in the placebo group versus 9/18 (50.0%) in the 30 mg QD group, 11/18 (61.1%) in the 30 mg BID group, and 14/18 (77.8%) in the 60 mg QD group. Of these, few were serious AEs (SAEs), with no treatment-related SAEs reported in the placebo group, 1/18 (5.6%) of the rentosertib 30 mg QD group, 2/18 (11.1%) in the 30 mg BID group and 2/18 (11.1%) in the 60 mg QD group (Table 2). The most common TEAEs were hypokalemia (2 in placebo (11.8%), 3 in 30 mg QD (16.7%), 5 in 30 mg BID (27.8%) and 3 in 60 mg QD (20.4%)), hepatic function abnormal (2 in placebo (11.8%), 2 in 30 mg QD (11.1%), 4 in 30 mg BID (22.2%), diarrhea (0 in placebo, 2 in 30 mg QD (11.1%), 3 in 30 mg BID (16.7%) and 5 in 60 mg QD (27.8%)) and alanine aminotransferase (ALT) increase (1 in placebo (5.9%), 1 in 30 mg QD (5.6%), 1 in 30 mg BID (5.6%), 6 in 60 mg QD (33.3%) and 3 in 60 mg QD (16.7%)). A complete listing of TEAEs is presented in Supplementary Table 2.

Table 2 AEs during treatment
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The most frequent reason for treatment discontinuation was AEs (12/16 (75%)), and the treatment of 7 of the 12 patients was discontinued for AEs due to liver injury or dysfunction (0/18 (0%) in the 30 mg QD group, 4/18 (22.2%) in the 30 mg BID group and 3/18 (17%) in the 60 mg QD group) (Supplementary Table 3). Four of the seven (57.1%) total participants who withdrew because of liver toxicity were concurrently administered nintedanib antifibrotic therapy. Three of the four (75%) remaining discontinuations were due to patient withdrawal from the trial (3/71 patients (4.2%)). One patient died of heart failure before the week 12 end-of-treatment (EOT) visit determined to be unrelated to the trial owing to a history of degenerative aortic valve disease, aortic valve incompetence, left ventricular dysfunction, aortic dilatation, pulmonary hypertension, increased blood glucose and hypercholesterolemia, as well as a lack of other AEs reported and no notable findings for vital signs, physical examination, 12-lead electrocardiogram (ECG) or laboratory tests since randomization. A complete listing of patients whose treatment was discontinued due to TEAE is presented in Supplementary Table 4.

Secondary lung function endpoints

FVC is the gold-standard metric for assessing the lung function of patients with IPF and response to therapeutic intervention33,34,35. After 12 weeks of treatment, patients who received placebo experienced deteriorating FVC with a mean change of −20.3 ml (95% confidence interval (CI) −116.1 to 75.6). Patients who received 30 mg rentosertib QD showed a similar mean reduction in FVC of −27.0 ml (95% CI −88.8 to 34.8), patients who received 30 mg rentosertib BID experienced a mean change of +19.7 ml (95% CI −60.5 to 99.9) and patients receiving 60 mg rentosertib QD showed improved FVC, with and +98.4 ml (95% CI 10.9 to 185.9) (Fig. 2 and Extended Data Fig. 2). Patients receiving 60 mg rentosertib QD not concurrently taking SOC antifibrotic therapy exhibited significant improvement in FVC (+187.8 ml, 95% CI 68.6 to 306.9 ml), whereas patients concurrently taking 60 mg rentosertib QD with either nintedanib or pirfenidone did not exhibit significant changes in FVC (Extended Data Fig. 3).

Fig. 2: Changes in FVC ± 95% CI after 12 weeks of rentosertib treatment compared to baseline.
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a, The absolute change in FVC ± 95% CI. b, The absolute change in FVC ± 95% CI ANCOVA model with multiple imputation assuming missing at random (MAR).

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The changes in DLCO and FEV1 were relatively small and similar across treatment regimens (Extended Data Fig. 4a–e). Changes in self-reported QOL and physical ability metrics, such as responses to the Leicester Cough Questionnaire (LCQ) and 6-min walk distance (6MWD), respectively, were likewise similar across treatment groups (Extended Data Fig. 4f–i), although our modeling indicated a significant increase of least-squares mean change of the LCQ scores of patients receiving 60 mg rentosertib compared with those receiving placebo (two-sided P = 0.0495).

Three patients (16.7%) receiving 60 mg rentosertib QD and one patient (5.9%) receiving placebo experienced acute exacerbation of IPF (AE-IPF) (Supplementary Table 5). The patients in the 60 mg QD group were all hospitalized in response to their acute exacerbations with a mean duration of 23.3 days, whereas the patient in the placebo group was not hospitalized.

Post-hoc exploratory analyses

Pharmacokinetics

A PK analysis of rentosertib concentration in the blood plasma of patients with IPF after single and multiple doses shows a favorable PK profile, with greater exposure in patients receiving 60 mg rentosertib QD compared with patients receiving 30 mg rentosertib QD or 30 mg rentosertib BID, peaking 1 h after administration (Fig. 3a–c and Supplementary Table 6). The area under the plasma–concentration curve (AUC0–t), measuring total exposure to the drug to the last available time point, was greater for the 60 mg rentosertib QD at both week 0 (first dose) and week 12 (EOT; arithmetic mean 1,630 and 3,450 h ng−1 ml−1, respectively) than for 30 mg rentosertib BID (315 and 1,390 h ng−1 ml−1) and 30 mg rentosertib QD (553 and 788 h ng−1 ml−1) (Fig. 3d, Extended Data Fig. 5a and Supplementary Table 6). The steady state was achieved at week 2, and no obvious drug accumulation was observed by week 12. The geometric mean half-life (t1/2) ranged from 10.9 to 12.0 h at week 12.

Fig. 3: Pharmacokinetic properties of rentosertib.
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a–c, Pharmacokinetic dynamics of rentosertib in patient sera collected predose and periodically throughout 24 h posttreatment at week 0 (a), at 0 h posttreatment at weeks 2, 4 and 8 (b) and 48 h after administration at week 12 (c). d, AUC0–t of rentosertib exposure; n = 22 in 30 mg QD, n = 21 in 30 mg BID, n = 20 in 60 mg QD. e, Cmax of rentosertib exposure; n = 22 in 30 mg QD, n = 21 in 30 mg BID, n = 20 in 60 mg QD. f, Ctrough of rentosertib exposure in patient sera collected pretreatment at weeks 0, 2, 4, 8 and 12. All data represent arithmetic mean ± s.d.

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A higher AUC0–t and t1/2 at week 12 for all dosages indicates an increased exposure to rentosertib with increases time on treatment (Fig. 3a,c and Extended Data Fig. 5a,b). Change in patients’ FVC from baseline to week 12 was positively correlated with AUC0–t and trough concentration (Ctrough) (Extended Data Fig. 5c,d). Not surprisingly, the Cmax exposure was greatest in patients receiving the 60 mg QD dose, with both 30 mg QD and 30 mg BID showing similarly lower Cmax (Fig. 3e), whereas Ctrough was similar between 30 mg BID and 60 mg QD dosages (Fig. 3f). Together, the PK dynamics suggest that higher dosage leads to increased exposure from higher initial net absorption, with a more pronounced effect by the end of the treatment period associated with slower clearance.

Biomarkers and proteomic signatures of response in blood

We performed proteomic profiling of patient serum samples to understand the mechanism of action of rentosertib, guide development of biomarkers of response and evaluate the hypothesis that inhibition of TNIK may target biological aging36. We identified a total of 2,841 proteins using the Olink Explore 3072 panel across serum samples collected from patients in all treatment groups at baseline, 2 weeks, 4 weeks and 12 weeks. Using a linear regression model to evaluate time-dependent effects of rentosertib treatment on protein expression changes, we found that treatment duration substantially impacted protein expression patterns, with 20, 82 and 192 proteins significantly changing with treatment at weeks 2, 4 and 12, respectively (Benjamini–Hochberg-adjusted P value <0.1) (Extended Data Fig. 6a). Paired statistical testing to assess dose-associated changes in protein expression compared with placebo identified 1, 8 and 22 high-confidence (Padj < 0.05) differentially abundant proteins in sera from patients receiving 30 mg QD, 30 mg BID and 60 mg QD rentosertib, respectively, using a generalized linear model (Fig. 4a,b and Supplementary Tables 7–9). Restricting analysis of differential proteins from baseline to week 12, the number of significantly altered proteins increased substantially with higher dosing: 0, 39 and 115 in the 30 mg QD, 30 mg BID and 60 mg QD groups, respectively (Extended Data Fig. 6b). Together, this suggests that the duration of rentosertib treatment and the dose are associated with changes in serum protein profiles.

Fig. 4: Post-hoc exploratory analysis of serum protein profiling.
figure 4

a,b, Differentially abundant proteins in serum from patients receiving rentosertib at 30 mg BID (a) and 60 mg QD (b) identified with a generalized linear model. c, Serum levels of COL1A1, FAP, FN1 and MMP10 decrease with rentosertib dose and time on treatment. n = 11 patients in placebo, n = 11 patients in 30 QD, n = 11 patients in 30 BID, n = 10 patients in 60 QD. d, The changes in serum levels of COL1A1, FAP, FN1 and MMP10 from baseline to week 12 are inversely correlated with change in FVC from baseline to week 12. n = 43 patients at each visit; P value calculated by Pearson correlation analysis. e,f, Reactome pathway enrichment for differentially abundant proteins in serum from patients receiving rentosertib at 30 mg BID (e) and 60 mg QD (f).

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Downregulated proteins associated with 30 mg BID and 60 mg QD treatment include known fibrosis-associated proteins such as MMP10, PTPRZ1, COL1A1, FAP, FN1, ROBO2, ASPN and LTBP2 (Fig. 4a–c and Supplementary Tables 7–10). Analyzing the differentially abundant proteins to find association with change in FVC found that ASPN, PTPRZ1, MMP10 and CHAD were both significantly inversely correlated to change in FVC (P < 0.05) and significantly downregulated after administration of rentosertib at the week 12 time point, suggesting an array of potential biomarkers for response to treatment (Fig. 4a–d and Supplementary Tables 7–9). Pathway enrichment analysis identified extracellular matrix organization as the most downregulated Reactome pathway gene set in both treatment groups, suggesting a reduction in excessive extracellular matrix production (Fig. 4e,f).

We queried diverse published gene expression datasets profiling the tissue of patients with IPF and that of healthy individuals using our AI-powered target discovery platform, PandaOmics37, for expression patterns of these top genes to validate their relevance to IPF. COL1A1, MMP10 and FAP were found to be universally upregulated in IPF patient samples compared with healthy individuals, and FN1 was upregulated in most datasets (Extended Data Fig. 7a).

We further found that the abundance at week 12 of proteins previously reported to be associated with lung function38 and transplant-free survival39 in patients with IPF, such as ADAMTSL2, COL6A3, CCN3, COL24A1, KRT19 and LTBP2, were associated change in FVC (Extended Data Fig. 7b). The expression of inflammation-associated pathways, other immune-related proteins, such as IL-10 and CD5, and other extracellular matrix-related proteins, such as COL6A3, were found to have significant associations with time on treatment and change in FVC, further suggesting that TNIK plays a diverse role in various aging-related dysregulated pathways36 (Fig. 4e,f and Extended Data Fig. 8).

Discussion

In this phase 2a study, treatment with rentosertib at 30 mg QD, 30 mg BID and 60 mg QD over 12 weeks was safe and well tolerated. Treatment-related SAEs were rare, with a similarly low rate across treatment and placebo groups. The most common TEAEs leading to discontinuation of treatment were liver toxicity related and occurred mostly in patients who were also treated with nintedanib antifibrotic therapy, although notably not pirfenidone. In addition to liver toxicities, diarrhea and hypokalemia were among the most common TEAEs among participants treated with rentosertib. To determine the respective and synergistic contributions of rentosertib and SOC antifibrotic therapies to liver and gastrointestinal toxicity, as well as any potential effects on absorption or trough levels, we require further drug–drug interaction studies and assessment in a larger cohort of mixed SOC antifibrotic recipients and nonrecipients.

Treatment with 60 mg rentosertib QD over 12 weeks was associated with a trend toward an increase in FVC in patients with IPF. The 60 mg rentosertib QD dose exhibited the greatest mean improvement in lung function, as measured by FVC, whereas patients receiving placebo experienced an average decline in FVC. Patients receiving 60 mg rentosertib QD experienced a mean increase in FVC percentage change of 2.82% by our modeling, meeting the minimal clinically important difference (MCID) reported for FVC in IPF of 2–6% (ref. 40). Subgrouping patients by concurrent SOC antifibrotic treatment showed patients receiving 60 mg rentosertib QD without SOC antifibrotic exhibited the strongest increase in FVC, suggesting potential interaction between the drugs that will be investigated in future trials. The incongruity of increased FVC with stable DLCO may be attributed to low numbers of patients in each treatment arm and the inherent variability of DLCO measurement41.

The impact of rentosertib treatment on QOL metrics was largely inconclusive, with large variances within treatment arms, although our modeling found that patient-reported coughing-related QOL (via LCQ) was significantly improved in patients receiving 60 mg rentosertib QD. As various QOL metrics integrate the functionality of a number of organ systems, of which lung health is one contributing factor, a lack of difference among the treatment groups may be due to the relatively short data collection period and small cohort size.

AE-IPF is a strong indication of dismal patient prognosis42 with a median survival of 2.2 months after onset43, and incidence informs long-term therapeutic efficacy. Three acute exacerbations occurred in the 60 mg rentosertib arm and one in the placebo arm; however, a 12-week study is a relatively short time frame to capture such long-term disease events or trajectory44. An unintended consequence of rentosertib’s potential immunomodulatory mechanism of action9 may be a dampened site-specific immune response, potentially leading to increased susceptibility to infection, a known trigger for a subset of AE-IPF45,46. Because AE-IPF is a clinically important occurrence, we intend to assess its incidence in longer trials with larger cohorts in the future, which would assess patients for at least 12 months to more fully capture the incidence of AE-IPF and other potential AEs.

Assaying the serum proteome in patients provided insight into changes in circulating proteins associated with higher doses of rentosertib. The top up- and downregulated proteins showed high overlap between the 30 mg BID and 60 mg QD doses, with downregulated proteins enriched for association with extracellular matrix organization, a key feature of fibrotic progression in IPF. Moreover, a number of the top downregulated proteins are known to be associated with pulmonary fibrosis: MMP1047, LTBP239,48,49, KRT1939 and COL24A139 have been nominated as biomarkers for IPF and transplant-free survival; PTPRZ150 activates β-catenin signaling, which contributes to IPF development51, and BHLHE40 regulates epithelial-to-mesenchymal transition in IPF via β-catenin52; COL1A1 is a key component of dysregulated extracellular matrix deposition characteristic of fibrosis53,54,55; ASPN plays a role in myofibroblast transformation56 and may play a central role in IPF tissue remodeling55,57; circulating ROBO2 is associated with poor IPF prognosis58; FAP is expressed exclusively in fibrotic loci in the lungs of patients with IPF59; and CHAD binds to type II collagen in cartilaginous fibrotic loci60,61. Downregulation of this IPF-associated protein profile supports the hypothesis that inhibition of TNIK modulates IPF pathophysiological pathways and points to a potential serum protein signature as a biomarker for response to rentosertib treatment.

The limitations of this study include the small cohort size of each arm, the geographical and demographic homogeneity of the participants (all were residents of China of similar race) and a short period of follow-up, which limit the assessment of long-term safety and efficacy. Despite the short duration and the number of withdrawals from the trial across all arms (n = 16/71 (22.5%)), the results are encouraging for additional study of this drug candidate and target, suggesting that rentosertib is generally safe and potentially effective and, more broadly, that using AI in both target identification and in drug design may enhance the efficiency of the drug development process. Longer phase 2 or 3 trials studying rentosertib in a larger, heterogeneous sample of patients with IPF from around the globe are warranted to further evaluate the efficacy and safety of rentosertib treatment for IPF.

Methods

Study design

This phase 2a, multicenter, double-blind, placebo-controlled, randomized, multidose trial of rentosertib in adults with IPF was performed at 21 sites across China beginning 19 July 2023 and running through 11 June 2024. Adults with IPF were randomly assigned in a 1:1:1:1 ratio via interactive response technology to receive oral rentosertib (at a dose of 30 mg (QD), 30 mg (BID) or 60 mg (QD)) or placebo (QD) for 12 weeks, along with the continued use of SOC medications (Fig. 1). Patient screening occurred, on average, 30 days before the first dosing at the second visit. Patients were clinically evaluated on day 1, week 2, week 4, week 8, week 12 (end of treatment) and week 13 (end of study). The study treatment rentosertib was provided by Insilico Medicine or a designated contract research organization, packaged and labeled in accordance with the principles of Good Manufacturing Practice. Resupply to the sites was managed via an interactive response technology system, which also monitored expiry dates of supplies available at the sites. Access to the randomization codes was controlled and documented.

The trial was conducted following the principles outlined in the Declaration of Helsinki and the International Council for Harmonization guidelines for Good Clinical Practice. The institutional review board or ethics committee at participating centers approved protocols and adhered to local laws before initiation of the clinical trial. All patients in this study provided written informed consent. Trial management and data processing, summarization and analyses were conducted by Fortrea Clinical Pharmacology Services, Leeds, UK.

Authors used by the study sponsor were involved in the trial design, collection, analysis and data interpretation. Subjects, investigators, site study staff, reviewers and everyone involved in study conduct or analysis were blinded with regard to the randomized treatment assignments until after data freeze. Bioanalytics staff were allowed to identify samples from subjects assigned to placebo treatment but did not disclose randomization until trial unblinding. These authors contributed to manuscript preparation across all iterations of the manuscript. The first and last authors of this study vouch for the completeness of all data reported in this publication.

Participants

Eligible patients were 40 years of age or older with an IPF diagnosis as defined by the American Thoracic Society, European Respiratory Society, Japanese Respiratory Society and Latin American Thoracic Association guidelines62. Enrolled patients presented with stable IPF and were deemed suitable for study participation on the basis of medical history, physical examination, vital signs, 12-lead ECG and laboratory evaluation. Patients previously receiving nintedanib or pirfenidone were enrolled if their antifibrotic therapy had been stable for >8 weeks before the first (screening) visit. Eligible patients had to meet all three of the following criteria during the screening visit: (1) FVC >40% predicted of normal, (2) DLCO corrected for hemoglobin ≥25% and <80% predicted of normal, and (3) FEV1/FVC ratio >0.7 based on prebronchodilator value. Patients were excluded for concomitant respiratory disorders or health concerns, including, but not limited to, cystic fibrosis, active aspergillosis, active tuberculosis, confirmed coronavirus disease 2019 (COVID-19) at visits 1 or 2 or severe COVID-19 requiring hospitalization within 6 months of visit 1 or long COVID-19, clinically relevant or severe pulmonary hypertension, acute IPF exacerbation within 4 months before visit 1 and/or during the screening period, upper or lower respiratory tract infection that has not fully resolved within 4 weeks before visit 1 and/or before visit 2 (day 1), active on the lung transplantation register or expected to become active on the lung transplant register within 6 months before visit 1, a history of lung volume reduction surgery or lung transplant, current smoker, BMI >40 kg m−2, aspartate aminotransferase (AST) or ALT ≥1.5 times upper level of normal or total bilirubin ≥1.5 times upper level of normal at visit 1, eGFR ≤60 ml min−1 1.73 m−2 (Chronic Kidney Disease Epidemiology Collaboration formula) at visit 1, uncontrolled hypertension (systolic pressure >160 mmHg or diastolic pressure >95 mmHg despite treatment with antihypertensive treatments) at visit 1, unstable cardiac angina or myocardial infarction within 6 months before visit 1, taking oral corticosteroids, any documented active or suspected malignancy or history of malignancy within 5 years before visit 1, known hypersensitivity or contraindications to serine/threonine kinase inhibitors, any condition or treatment possibly affecting drug absorption (for example, gastrectomy or metoclopramide), taking restricted medications (for example, moderate-to-strong CYP3A4–CYP1A2 inhibitors or inducers or medications primarily metabolized by CYP3A4–CYP1A2) or consumption of grapefruit or grapefruit juice, pomelo, Seville orange or Seville orange-containing products within 48 h before day 1, substantial trauma or major surgery within 3 months before visit 1 or planned major surgery during the study and 12-lead ECG demonstrating corrected QT interval by Fridericia (QTcF) >450 ms for males and >470 ms for females, or a QRS interval >120 ms at visit 1. Full inclusion and exclusion criteria are detailed in the associated study protocol.

Given an approximate sample size of 15 subjects per treatment arm, there exists a 90% probability of observing at least one AE if the true population rate is approximately 15%, which is sufficient to assess the feasibility of safety parameters.

Objectives and endpoints

The primary objective of this study was to evaluate the safety and tolerability of orally administered rentosertib for up to 12 weeks in adult patients with IPF compared with placebo. The primary endpoint for this study was the percentage of patients who have at least one TEAE. Patients were continually monitored for AEs during the 12 weeks of rentosertib treatment and for 1 week after administration of the final dose, with study drug-related SAE and AEs of special interest collected if they occurred beyond the study period.

The secondary efficacy endpoints measured the relative and percent change in FVC from week 0/visit 2 up to week 12. The absolute and relative change in FVC (%) predicted from week 0/visit 2 up to week 12 as well as the change in DLCO (%) predicted from week 0 up to week 12 was also measured. Twenty-one subjects (29.6%) had FVC and FEV1 measured at baseline (randomization with spirometry equipment sourced from the local site meeting the 2019 American Thoracic Society and European Respiratory Society guideline criteria)63, provided that the subject’s baseline and end-of-treatment spirometry were performed with the same equipment. Fifty subjects (70.4%) had FVC and FEV1 measured at baseline/randomization with centrally sourced SpiroSphere devices (Clario) provided by the sponsor with study-specific training and proficiency tests to the operating staff to meet the 2019 ATS/ERS guideline criteria63 with central overread. All DLCO measurements were assessed with devices from each site according to the ATS/ERS guidelines64, and all measurements were conducted with the same equipment for each site. FVC MCID has been reported to be a change of 2–6% in patients with IPF40.

The change in LCQ, a 19-item questionnaire that assesses cough-related QOL65, from week 0 to weeks 4, 8 and 12 was also analyzed. The LCQ examines three domains (physical, psychological and social) related to the patient’s experience within a 24-h time frame. The range for the total score on the LCQ is 3–21, with the sum of each domain score ranging from 1 to 7. A higher score indicates a better QOL, with an MCID reported to be a change of 1.3 points for patients with chronic obstructive pulmonary disease66. The LCQ questionnaire has mainly been validated in disease settings such as chronic obstructive pulmonary disease67, noncystic fibrosis bronchiectasis68 and chronic cough69.

The change in 6MWD in meters from week 0 to week 12 was also evaluated.

Other secondary endpoints included pharmacokinetic parameters of rentosertib and related metabolites after the first dose at visit 2 and the last dose at visit 6 (EOT). This study also collected data on the number of acute IPF exacerbations and the number of days hospitalized after acute IPF exacerbations from week 0 through week 12.

The exploratory endpoints analyzed include the change in IPF blood biomarkers after rentosertib treatment at weeks 0, 2, 4, 8 and 12. Changes in the blood proteome in treated patients were also measured at weeks 0, 2, 4, 8 and 12.

Statistical analysis

An analysis of covariance (ANCOVA) model was performed for endpoints including FVC. The model includes treatment as a fixed effect and baseline value as a covariate. LCQ scores were analyzed using a mixed model for repeated measures. FVC and other spirometry measurements were collected at baseline and at week 12, with all patients being measured at baseline and a total of 22 patients missing week 12 measurements (5 receiving 30 mg rentosertib QD, 6 receiving 30 mg rentosertib BID, 8 receiving 60 mg rentosertib QD and 3 receiving placebo). Missing values at week 12 were imputed using a multiple imputation method assuming missing at random. All statistical comparisons were made using two-sided tests. No statistical comparisons of AEs between treatment groups were performed. Given an approximate sample size of 15 subjects per treatment arm, there exists a 90% probability of observing at least one AE if the true population rate is approximately 15%, which was sufficient to assess the feasibility of safety parameters, although a sample size calculation based on statistical power considerations was not performed. Spearman correlation analysis was used to assess the association between FVC change and AUC0–t or Ctrough.

The safety population included all subjects who received at least one dose of study treatment. The intent-to-treat population included any randomized subjects. The intent-to-treat population was used for summary of demographic and baseline characteristics and for analysis of secondary endpoints, except PK analysis. Assessment of PK parameters included subjects who received at least one dose of study treatment and had at least one measurable concentration collected after dosing. Assessment of serum biomarkers was performed on subjects who had a predose baseline value and at least one value after treatment initiated.

For Olink proteomics analyses, we utilized a generalized linear mixed model to assess the association between each protein and dose or FVC change. The formula for modeling the treatment effect is

$${{\mathrm{NPX}}}\left({{\mathrm{protein}}\; {\mathrm{level}}}\right) \approx {{\mathrm{treatment}}}+{{\mathrm{visit}}}+{{\mathrm{treatment}}}:{{\mathrm{visit}}}+\left(1|{{\mathrm{PatientID}}}\right).$$

The lme4 package was used for the generalized linear mixed model analysis. For modeling the FVC changes, we conducted the statistical analyses in two steps. In the first step, we regressed the protein level (NPX) against the visit to obtain the slope of protein change rate for each individual. In the second step, we regressed the FVC changes against the slope of protein change rates obtained from the first step. This was conducted using the lm package. Pathway analyses were performed using the clusterProfiler R package, with Reactome processes70 as the curated pathway set.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.