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. 2026 Jul 10;26:1187. doi: 10.1186/s12885-026-16520-2

Safety and tolerability of PARP inhibitors in cancer patients: a network meta-analysis of randomized controlled trials

Chenyu Wei 1,3, Shujing Liu 1, Jiexuan Hu 1, Bangwei Cao 1,3,✉, Bing Liu 2,✉
PMCID: PMC13637266  PMID: 42432595

Abstract

Background

Poly(ADP-ribose) polymerase inhibitors (PARPi) for the treatment of homologous recombination (HR)-deficient cancers have revolutionized cancer treatment in recent years. Despite documented clinical benefits, the use of PARPi is associated with several adverse events (AEs) inducing poor prognosis. The aim of our study was to evaluate the safety and tolerability of different PARPi and PARPi-based therapies via a network meta-analysis based on randomized controlled trials (RCTs).

Methods

We systematically searched and assessed RCTs in PubMed, Embase, the Cochrane Library, and the ClinicalTrials.gov registry from inception to 1st March 2026. Studies assessed the safety of PARP inhibitors or PARPi-based therapies were included. The primary outcomes were serious AE and discontinuation of treatment due to AE. For statistical analysis, the fixed-effect or random‐effect model was used, depending on the heterogeneity (I2).

Results

Sixty-seven studies with 23,285 patients were included in the pooled safety analysis. All the 6 included PARPi were proven to have a higher risk of serious AE and AE-related discontinuation of treatment compared with placebo. Olaparib (OR, 0.21; 95% CI, 0.05–0.85) and pamiparib (OR, 0.14; 95% CI, 0.02–0.91) had significant lower risk from suffering serious AE compared with senaparib. Discontinuation of treatment did not differ significantly among the 6 included PARP inhibitors. The 5 PARPi-based therapies included in our study showed similar risk of serious AE. PARPi plus chemotherapy (OR, 2.56; 95% CI, 1.47–4.55) might increase significantly the risk of AE leading to discontinuation compared with PARPi alone. PARPi plus anti-angiogenic drug (OR, 3.66; 95% CI, 1.10-12.19) and PARPi plus chemotherapy (OR, 4.76; 95% CI, 1.54–14.29) had significant higher risk of AE-related discontinuation compared with PARPi plus ARSI.

Conclusion

In our study, pamiparib demonstrates the most favorable safety profile among evaluated PARP inhibitors relatively, while senaparib might be associated with a higher incidence of AEs. PARPi plus anti-angiogenic drug or chemotherapy might cause the most occurrence of AEs relatively. Therefore, on the basis of balancing efficacy and safety, selecting optimal PARPi and formulating tailored combination therapy for individual patients is necessary.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12885-026-16520-2.

Keywords: PARP inhibitor, Adverse event, Safety profile, Cancer, Network meta-analysis

Introduction

Poly(ADP-ribose) polymerase inhibitors (PARPi) for the treatment of homologous recombination (HR)-deficient cancers have revolutionized cancer treatment in recent years [1]. Indeed, many PARPi have already been granted regulatory approvals, either as monotherapy or in combination with other agents, and consequently reached clinical practice [2, 3].

PARPi target tumor cells with defects in homologous recombination repair (HRR), which plays a crucial role in maintaining genomic stability, especially PARP1, PARP2 and PARP3 [4, 5]. These drugs bind to PARP enzyme cofactor, inhibiting autoPARylation, and trap PARP in DNA damage sites which leads to cell death in double-strand repair-deficient cancer cells [6]. The most significant target gene is Breast Cancer Susceptibility Genes (BRCA). It is known that BRCA1 and BRCA2 are tumor suppressor genes involved in DNA repair, which are closely related to the incidence of breast cancer, ovarian cancer and prostate cancer [7]. Cancer cells with these target genes are particularly sensitive to PARPi. Meanwhile, several PARP inhibitors (olaparib, talazoparib, niraparib, and rucaparib) have been proven to have favorable prognosis and previous network meta-analyses have reported similar survival benefits between these drugs [8–10].

Despite documented clinical benefits, the use of PARPi is associated with several adverse events (AEs), including myelosuppression, fatigue, gastrointestinal dermatologic, neurologic and cardiac toxicity [11–13]. A previous network meta-analysis has demonstrated that niraparib is linked to an elevated incidence of thrombocytopenia and neutropenia. Among all evaluated agents, olaparib shows the greatest likelihood of triggering decreased appetite, vomiting and nasopharyngitis. Meanwhile, rucaparib tends to increase the risks of anemia, vomiting, abdominal pain, dyspepsia, dysgeusia, and arthralgia [12]. Most of the AEs can be well controlled or even reversed by appropriate management. However, several AEs are severe, like cardiac related AEs and thromboembolism events, which may affect the treatment and lead to poor prognosis. Since each PARP inhibitors has different binding affinities for PARP1, PARP2, and PARP3, the target effects of PARP inhibition can be varied among these drugs [14].

BRCA1/2 gene defects fail to achieve favorable clinical responses to such agents. Furthermore, a substantial number of patients tend to develop acquired resistance following long-term oral intake of PARP inhibitors. Apart from the above situations, several other influencing factors also contribute to the emergence of PARP inhibitor resistance, including reversion mutations, epigenetic modification, restoration of ADP-ribosylation (PARylation) and pharmacological alteration lead to PARPi resistance [15]. Notably, even if tumors show an initial therapeutic response to PARP inhibitors, they can gradually develop drug resistance via multiple biological pathways. Over 40% of patients with rational combination strategies involving PARPi have been explored and are in various stages of clinical development [16]. At present, several large-scale trials have shown AEs induced by different PARPi-based combination regimens are diverse. Initial combinations involving PARPi with chemotherapy were hindered by significant overlapping hematologic toxicity, but newer combinations with fewer toxicities and more targeted approaches are undergoing evaluation [16]. Despite the potential benefit of these combination strategies involving PARPi, there has been no articles that systematically explain the comparative toxicity of them currently.

Here, we aimed to conduct a network meta-analysis based on randomized controlled trials (RCTs) to compare the safety and tolerability among different PARP inhibitors and PARPi-based therapies.

Methods

This network meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We have registered on PROSPERO (CRD 42024595836) for our study.

Search strategy and selection criteria

Literature retrieval was performed across PubMed, Embase and Cochrane Library databases, covering all publications published up to 1st March 2026. The retrieval keywords consisted of PARP inhibitors, olaparib, talazoparib, veliparib, niraparib, rucaparib, pamiparib, senaparib and fluzoparib. Additionally, we manually retrieved eligible trials from the clinicaltrials.gov platform and checked reference citations of retrieved published papers to supplement more qualified researches. The detailed full search formulas are presented in Supplementary Appendix S1. Eligibility screening for all retrieved literature was conducted in strict accordance with the PICOS framework. Studies satisfying all the following screening standards were enrolled in this analysis: (1) Population: patients diagnosed with any type of solid tumors; (2) Intervention: trials adopting single PARP inhibitor treatment or combination regimens centered on PARP inhibitors; (3) Comparison: controlled groups receiving placebo therapy or treatments without PARP inhibitors; (4) Outcome: studies focused on the safety profiles of PARP inhibitor-related therapies. Serious AE and discontinuation of treatment due to AE were defined as primary endpoints. The secondary outcomes were as follows: any grade AE, grade 3–5 AE, interruption of treatment due to AE, dose reduction due to AE, thromboembolic events, cardiovascular events, hypertension, pneumonia, new primary malignancy, and myelodysplastic syndrome or acute myeloid leukemia (MDS/AML); (5) Study designs: phase II and III RCTs. Case reports, case series, case-control studies, observational researches, single-arm clinical trials and non-randomized clinical trials were ruled out. Phase I clinical trials were also excluded owing to limited sample size and inconsistent trial design. In cases where multiple articles reported data from the same clinical trial, the latest published and most comprehensive literature was selected for final data extraction.

Data extraction

Two authors (CW and JH) independently extracted the following information from each trial using a standardized spreadsheet: first author, publication year, study phase, number of patients, cancer type, treatment regimens in intervention and control arms. Discrepancies were resolved by tracing them back to the original study and discussing them until an agreement was reached. If multi-arm trials were included, arms with the same regimen but different dose were integrated as one group to conduct the following analyses.

Quality assessment

All included RCTs were assessed via Cochrane Risk of Bias tool v.2 (RoB2). The risk of bias assessment covered five core dimensions, namely randomization process, deviations from intended intervention, missing outcome data, measurement of the outcome, and selection of the reported results. According to the above evaluation standards, each RCT was categorized into three bias risk levels: low risk, some concerns and high risk. Two authors (CW and JH) completed the study quality evaluation separately, and any inconsistent evaluation results were settled through discussion with a third reviewer (BC).

Statistical analysis

Statistical analyses were performed using R Studio package “netmeta” (version 4.2.0), which implements, according to the electrical network theory, a frequentist method based on a graph-theoretical approach [17]. R software (version 4.2.2) and “meta” R package was also used for the safety analysis of specific AEs. We used either common effects or random effects model to calculate pooled odds ratios with 95% CI for the comparison of AE between arms. Heterogeneity among included studies was assessed using the I2 statistic and the magnitude of the heterogeneity variance parameter (τ2). If there was a significant heterogeneity (I2 > 50% or τ2 > 0.16), a random effect model was chosen, otherwise a common effect model was used. Our standard for statistical significance was P < 0.05. For several endpoints, some treatment groups observed zero events. Due to the poor performance of the Wald confidence interval of the standard rate when the probability approaches 0 or 1, we used the Agresti Full method to perform continuity correction on the zero-event study to calculate a more reliable confidence interval. We ranked the interventions and control by P-score. P-scores range from 0 (worst) to 1 (best), measuring the mean extent of certainty that one treatment is better than another, averaged over all competing treatments. Rankings derived from P-scores were relative, model-based, and uncertain. To further increase credibility of our results, cumulative probability plots and surface under cumulative ranking (SUCRA) probabilities were also used to rank treatments. Higher SUCRA scores correspond to higher probability of a treatment being in the top ranks (i.e., a lower probability of having safety events). A comparison-adjusted funnel plot in combination with Egger’s test was used to evaluate potential publication bias. We evaluated the transitivity hypothesis based on prior biological and clinical knowledge, and identified sample size, cancer type and trial phase as potential effect modifying factors. A series of sensitivity analyses was conducted to explore the impact of the former.

Results

Study selection and characteristics

We identified 1886 records after the removal of duplicates followed by a screening of titles and abstract reviews, the full text of 198 articles was subsequently reviewed. Finally, 67 studies were included in this network meta-analysis. The flow chart was summarized in the PRISMA diagram (Fig. 1).

Fig. 1.

Fig. 1

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The flow diagram of the network analysis

Table S1 demonstrated the characteristics of the included RCTs, which were published between 2010 and 2025. For the 23,285 patients included in total for safety analysis, there were 11 RCTs with breast cancer [18–29], 1 with colorectal cancer [30], 4 with endometrial cancer [31–35], 3 with gastric cancer [36–38], 10 with lung cancer [39–48], 1 with melanoma [49], 24 with ovarian cancer [50–78], 2 with pancreatic cancer [79, 80], 8 with prostate cancer [81–88], 1 with soft tissue sarcoma [89], and 2 with urothelial carcinoma [90, 91]. The network meta-analysis that consisted of 22 studies served placebo as the control group to compare with 7 kinds of PARP inhibitors. Of the 22 trials, 9 used olaparib as the intervention, 6 used niraparib, 3 used rucaparib, 1 used fluzoparib, 1 used pamiparib, 1 used senaparib and 1 used veliparib. In addition, 45 RCTs with 10 different interventions were comprised to compare relative safety of universal PARPi-based treatments. The 10 interventions were as follows: anti-angiogenic drug, androgen receptor signalling inhibitor (ARSI), chemotherapy, immune checkpoint inhibitor (ICI), PARPi, PARPi plus anti-angiogenic drug, PARPi plus ARSI, PARPi plus chemotherapy, PARPi plus ICI and placebo.

Risk of bias

According to the RoB2 tool, a total of 39 RCTs showed low risk of bias, 25 RCTs showed some concerns risk of bias and 3 RCTs showed high risk of bias (Figure S1). All trials were assessed as low risk in randomization process, measurement of the outcome and selection of the reported result. Even though some of the trials were open-label, it was considered that compliance assessment was unaffected by the awareness of participants or investigators to a certain extent, because of the objective outcome assessment. Therefore, 25 open-label RCTs showed some concerns risk of bias in deviation from intended interventions. There were still trials showed high risk of bias because of poor performance or no relevant content in deviation from intended interventions.

Comparison of different PARP inhibitors

Serious AE and AE-related discontinuation of treatment

Serious AE of PARP inhibitors could be assessed in 17 trials (I2 = 68%, τ²=0.25). Fig. 2A showed the network plot of 6 interventions nodes. As indicated in Fig. 3A, all 6 PARP inhibitors showed rising trend of serious AE in relative to the placebo. According to the net league table (Fig. 4A), olaparib (OR, 0.21; 95% CI, 0.05–0.85) and pamiparib (OR, 0.14; 95% CI, 0.02–0.91) had significant lower risk from suffering serious AE compared with senaparib. Fig. 5A provided a graphical summary of the P-scores, which showed senaparib (0.049) as the highest probability of having serious AE, followed by niraparib (0.337), fluzoparib (0.373), rucaparib (0.515), olaparib (0.571) and pamiparib (0.735). The cumulative probability plots (Fig. 6A) and SUCRA scores (Figure S2A) suggested similar results. The funnel plot (Figure S3A) showed roughly symmetric distribution, while egger’s test illustrated publication bias in this analysis (P = 0.015). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. High heterogeneity was found in the large-scale trial subgroup, ovarian cancer subgroup and phase III subgroup (Table S2).

Fig. 2.

Fig. 2

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Network plots of comparisons for (A) serious AE, (B) AE-related discontinuation of treatment, (C) any AE, (D) grade 3–5 AE, (E) AE-related interruption of treatment, and (F) AE-related dose reduction of treatment due to different PARP inhibitors. AE: adverse event; PARP: Poly(ADP-ribose) polymerase

Fig. 3.

Fig. 3

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Forest plots of comparisons for odds ratio of (A) serious AE, (B) AE-related discontinuation of treatment, (C) any AE, (D) grade 3–5 AE, (E) AE-related interruption of treatment, and (F) AE-related dose reduction of treatment due to different PARPi-based therapies. AE: adverse event; PARP: Poly(ADP-ribose) polymerase

Fig. 4.

Fig. 4

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Net league table of head-to-head comparisons of serious AE and AE-related discontinuation of treatment due to (A) different PARP inhibitors and (B) different PARPi-based therapies. Each blue cell gives the effect of the column-defining intervention relative to the row-defining intervention. Each green cell gives the effect of the row-defining intervention relative to the column-defining intervention. AE: adverse event; PARPi: Poly(ADP-ribose) polymerase inhibitor; ARSI: androgen receptor signalling inhibitor; ICI: immune checkpoint inhibitor

Fig. 5.

Fig. 5

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Summary of P-scores for (A) AEs due to different PARP inhibitors and (B) AEs due to different PARPi-based therapies. AE: adverse event; MDS: myelodysplastic syndrome; AML: acute myeloid leukemia; PARPi: Poly(ADP-ribose) polymerase inhibitor; ARSI: androgen receptor signalling inhibitor; ICI: immune checkpoint inhibitor

Fig. 6.

Fig. 6

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Cumulative probability plots of (A) serious AE due to different PARP inhibitors, (B) AE-related discontinuation of treatment due to different PARP inhibitors, (C) serious AE due to different PARPi-based therapies, and (D) AE-related discontinuation of treatment due to different PARPi-based therapies. AE: adverse event; PARPi: Poly(ADP-ribose) polymerase inhibitor; ARSI: androgen receptor signalling inhibitor; ICI: immune checkpoint inhibitor

AE leading to discontinuation could be assessed in 19 trials (I2 = 51%, τ²=0.38). The network plot was shown in Fig. 2B. The included 6 PARP inhibitors showed a trend of higher risk of discontinuation due to AE compared with placebo (Fig. 3B). There was no significant difference among the 6 PARP inhibitors in this analysis according to the net league table (Fig. 4A). The P-scores showed a consistent ranking as the result of relative effects (Fig. 5A). Niraparib (0.636) had the lowest probability of leading to AE-related discontinuation of treatment, while senaparib (0.192) had the highest probability. The cumulative probability plots and SUCRA scores were shown in Fig. 6B and Figure S2A. No evidence of publication bias was found through the visualization of funnel plots (Figure S3B) and the results of Egger’s test (P = 0.456). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. High heterogeneity was found in the large-scale trial subgroup, ovarian cancer subgroup and phase III subgroup (Table S2).

Any AE and grade 3–5 AE

A total of 16 trials comprising 7 interventions were included in the any AE analysis (I2 = 0%, τ²=0). Fig. 2C showed the network plot of 7 interventions nodes. All the PARP inhibitors showed a trend of higher risk of inducing AE except pamiparib compared with placebo (Fig. 3C). Notably, for head-to-head comparisons (Figure S4A), niraparib caused more AEs statistically significant compared with pamiparib (OR, 15.82; 95% CI, 3.88–64.49), olaparib (OR,4.99; 95% CI, 2.83–8.80), veliparib (OR, 4.75; 95% CI, 1.67–13.52), fluzoparib (OR, 4.35; 95% CI, 1.04-20.00) and rucaparib (OR, 3.00; 95% CI, 1.22–7.40). Pamiparib had significant lower risk of any AE compared with rucaparib (OR, 0.19; 95% CI, 0.04–0.86). The P-scores showed that pamiparib (0.917) had the lowest probability of leading to AE, followed by olaparib (0.552), veliparib (0.523), fluzoparib (0.494), rucaparib (0.412), senaparib (0.331) and niraparib (0.049) (Fig. 5A). The cumulative probability plots (Figure S5A) and SUCRA scores (Figure S2A) suggested similar results. No evidence of publication bias was found through the visualization of funnel plots (Figure S3C) and the results of Egger’s (P = 0.085).

Twenty-one RCTs reported grade 3–5 AEs of 7 different PARP inhibitors (I2 = 75%, τ²=0.25). Fig. 2D showed the network plot of 7 interventions nodes. All 7 PARP inhibitors showed rising trend of serious AE relative to the placebo (Fig. 3D). In the net league table (Figure S4A), niraparib had significant higher risk of grade 3–5 AE compared with olaparib (OR, 2.60; 95% CI, 1.35–5.03) and pamiparib (OR, 4.23; 95% CI, 1.15–15.60). Besides, olaparib also led to less grade 3–5 AEs than rucaparib (OR, 0.40; 95% CI, 0.17–0.92). Fig. 5A also illustrated pamiparib (0.789) as the lowest probability of suffering grade 3–5 AE, followed by veliparib (0.703), olaparib (0.643), rucaparib (0.265), niraparib (0.245), fluzoparib (0.209) and senaparib (0.199). The cumulative probability plots (Figure S5B) and SUCRA scores (Figure S2A) suggested similar results. The funnel plot (Figure S3D) showed a symmetric distribution, and Egger’s test revealed no obvious publication bias (P = 0.823). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Findings failed to explain the high heterogeneity (Table S2).

AE-related interruption and dose reduction of treatment

A total of 15 trials comprising 6 interventions were included in the AE leading to interruption of treatment analysis (I2 = 83%, τ²=0.25). The network plot was shown in Fig. 2E. As Fig. 3E presented, all PARP inhibitors were associated with a statistically significant higher risk of interruption of treatment due to AE compared with placebo. No statistically significant difference was found among 6 PARP inhibitors (Figure S4B). The P-scores showed a consistent ranking as the result of relative effects (Fig. 5A). Pamiparib (0.780) might have the lowest probability of leading to AE-related interruption of treatment, while senaparib (0.222) might have the highest probability. The cumulative probability plots and SUCRA scores were shown in Figure S5C and Figure S2A. The funnel plot (Figure S3E) showed a symmetric distribution, and Egger’s test revealed no obvious publication bias (P = 0.695). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. High heterogeneity was found in the large-scale trial subgroup, ovarian cancer subgroup and phase III subgroup (Table S2).

Seventeen trials reported dose reduction of treatment due to AE of the 6 PARP inhibitors (I2 = 69%, τ²=0.33). The network plot was shown in Fig. 2F. All PARP inhibitors showed statistically significant higher risk of dose reduction due to AE relative to the placebo (Fig. 3F). No statistically significant difference was found among the 6 PARP inhibitors (Figure S4B). The P-scores showed a consistent ranking as the result of relative effects (Fig. 5A). Olaparib (0.688) might have the lowest probability of leading to AE-related dose reduction, while fluzoparib (0.154) might have the highest probability. The cumulative probability plots and SUCRA scores were shown in Figure S5D and Figure S2A. The symmetrical funnel plot (Figure S3F) and Egger’s test (P = 0.165) indicated no publication bias in this network analysis. Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. High heterogeneity was found in the small-scale trial subgroup, ovarian cancer subgroup, phase III and phase II subgroup (Table S2).

Specific AE

The forest plots of the 5 specific AEs induced by 5 different PARP inhibitors were demonstrated in Figure S6. According to the P-scores (Fig. 5A), rucaparib (0.798) was the safest for thromboembolic events among the 5 PARP inhibitors, while niraparib (0.357) and olaparib (0.376) would increase this risk relatively. Niraparib (0.001) was the most toxic drug for hypertension, while olaparib (0.789) and rucaparib (0.794) were relatively safer. Senaparib (0.856) showed the lowest risk for cardiovascular events, while olaparib (0.249) showed the highest risk. Rucaparib (0.930) had the lowest probability of inducing pneumonia. The cumulative probability plots (Figure S7) and SUCRA scores (Figure S2A) suggested consistent outcomes.

Figure 7 showed that PARPi as a whole did not increase the risk of thromboembolic events, cardiovascular events, pneumonia and MDS/AML compared with placebo. Of note, PARPi was associated with a significantly increased risk of hypertension (OR, 2.33; 95% CI, 1.11–4.87). Interestingly, patients given PARPi decreased the risk of suffering new malignancy (OR, 0.66; 95% CI, 0.44–0.99). Given trial follow-up limitations and lack of biological plausibility, this result should be framed as observations requiring further validation.

Fig. 7.

Fig. 7

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Forest plots for odds ratio of (A) thromboembolic events, (B) cardiovascular events, (C) hypertension, (D) pneumonia, (E) new primary malignancy and (F) MDS/AML with PARP inhibitors versus placebo. MDS: myelodysplastic syndrome; AML: acute myeloid leukemia; PARP: Poly(ADP-ribose) polymerase

Comparison of different PARPi-based therapies

Serious AE and AE-related discontinuation of treatment

A total of 44 trials comprising 8 interventions were included in this analysis (I2 = 60%, τ²=0.10). The network plot was shown in Fig. 8A. All the treatment regimens showed a trend of higher risk of inducing AE compared with placebo (Fig. 9A). According to the net league table (Fig. 4B), PARPi plus anti-angiogenic drug, PARPi plus ARSI, PARPi plus chemotherapy, PARPi plus ICI, showed no significant risk of increasing serious AE compared with PARPi alone. PARPi plus chemotherapy (OR, 0.40; 95% CI, 0.17–0.93) might decrease significantly the risk of serious AE compared with PARPi plus ICI. Among the 5 PARPi-based therapies, the P-scores showed that PARPi plus chemotherapy (0.658) had the lowest probability of leading to serious AE, while PARPi plus ICI (0.135) had the highest probability (Fig. 5B). The cumulative probability plots (Fig. 6C) and SUCRA scores (Figure S2B) suggested similar results. No evidence of publication bias was found through the visualization of funnel plots (Figure S8A) and the results of Egger’s (P = 0.502). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Only prostate cancer subgroup decreased the heterogeneity significantly (Table S2).

Fig. 8.

Fig. 8

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Network plots of comparisons for (A) serious AE, (B) AE-related discontinuation of treatment, (C) any AE, (D) grade 3–5 AE, (E) AE-related interruption of treatment, and (F) AE-related dose reduction of treatment due to different PARPi-based therapies. AE: adverse event; PARPi: Poly(ADP-ribose) polymerase inhibitor; ARSI: androgen receptor signalling inhibitor; ICI: immune checkpoint inhibitor

Fig. 9.

Fig. 9

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Forest plots of comparisons for odds ratio of (A) serious AE, (B) AE-related discontinuation of treatment, (C) any AE, (D) grade 3–5 AE, (E) AE-related interruption of treatment, and (F) AE-related dose reduction of treatment due to different PARPi-based therapies. AE: adverse event; PARPi: Poly(ADP-ribose) polymerase inhibitor; ARSI: androgen receptor signalling inhibitor; ICI: immune checkpoint inhibitor

AE leading to discontinuation of treatment could be assessed in 50 trials (I2 = 47%, τ²=0.12). Fig. 8B showed the network plot of 8 interventions nodes. The included 8 interventions showed a trend of higher risk of discontinuation due to AE compared with placebo except ARSI (OR, 0.83; 95% CI, 0.42–1.63) (Fig. 9B). For head-to-head comparisons (Fig. 4B), PARPi plus chemotherapy (OR, 2.56; 95% CI, 1.47–4.55) might increase significantly the risk of AE leading to discontinuation compared with PARPi alone. PARPi plus anti-angiogenic drug (OR, 3.66; 95% CI, 1.10-12.19) and PARPi plus chemotherapy (OR, 4.76; 95% CI, 1.54–14.29) had significantly higher risk of AE-related discontinuation compared with PARPi plus ARSI. Among the 5 PARPi-based therapies, PARPi plus chemotherapy (0.058) and PARPi plus anti-angiogenic drug (0.155) obtained the two lowest P-scores, and PARPi plus ARSI obtained the highest score (0.692) (Fig. 5B). The cumulative probability plots and SUCRA scores were shown in Fig. 6D and Figure S2B. The funnel plot (Figure S8B) showed a symmetric distribution, and Egger’s test suggested no obvious publication bias (P = 0.613). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Only prostate cancer subgroup decreased the heterogeneity significantly (Table S2).

Any AE and grade 3–5 AE

Any AE of PARPi-based therapies were referred in 46 trials (I2 = 51%, τ²=0.30). Fig. 8 C showed the network plot of 8 interventions nodes. As indicated in Fig. 9C, all 8 regimens showed rising trend of any AE relative to placebo. There was no significant difference among the 5 PARPi-based therapies for any AE in the net league table (Figure S9A). Meanwhile, PARPi plus ARSI might have the lowest probability of leading to any AE according to the P-scores (0.482) and PARPi plus chemotherapy showed relative higher probability (0.133) (Fig. 5B). The cumulative probability plots and SUCRA scores were shown in Figure S10A and Figure S2B. The funnel plot (Figure S8C) showed a symmetric distribution, and Egger’s test revealed no obvious publication bias (P = 0.423). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Only prostate cancer subgroup decreased the heterogeneity significantly (Table S2).

Fifty-four trials reported grade 3–5 AE of the 9 different therapies (I2 = 86%, τ²=0.44). Fig. 8D showed the network plot of 9 interventions nodes. All these therapies showed rising trend of serious AE relative to placebo (Fig. 9D). In the net league table, PARPi alone had significantly lower risk of grade 3–5 AE compared with PARPi plus anti-angiogenic drug (OR, 0.41; 95% CI, 0.20–0.86) and PARPi plus chemotherapy (OR, 0.27; 95% CI, 0.15–0.50) (Figure S9B). Among the 5 PARPi-based therapies, the P-scores showed that PARPi (0.627) might have the lowest probability of leading to grade 3–5 AE, while PARPi plus chemotherapy (0.065) might have the highest probability of inducing grade 3–5 AE (Fig. 5B). The cumulative probability plots (Figure S10B) and SUCRA scores (Figure S2B) suggested consistent results. Egger’s test (P = 0.867) suggested no publication bias, while funnel plot showed an unsymmetric distribution (Figure S8D). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Only endometrial cancer subgroup decreased the heterogeneity significantly (Table S2).

AE-related interruption and dose reduction of treatment

A total of 27 trials comprising 6 interventions were included in the AE leading to interruption of treatment analysis (I2 = 87%, τ²=0.43). The network plot was shown in Fig. 8E. As Fig. 9E presented, all therapies were associated with a statistically significant higher risk of interruption of treatment due to AE compared with placebo. According to the net league table (Figure S9C), no significant difference was found among the PARPi, PARPi plus ARSI, PARPi plus chemotherapy and PARPi plus ICI. In the 4 PARPi-based therapies, the P-scores showed that PARPi plus chemotherapy (0.472) might have the lowest probability of AE leading to interruption, followed by PARPi (0.235) and PARPi plus ARSI (0.220), while PARPi plus ICI (0.190) had the highest probability (Fig. 5B). The cumulative probability plots (Figure S10C) and SUCRA scores (Figure S2B) suggested similar results. The funnel plot (Figure S8E) and Egger’s test showed no obvious publication bias (P = 0.477). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Findings failed to explain the high heterogeneity (Table S2).

Thirty-four trials reported dose reduction of treatment due to AE of 6 therapies (I2 = 81%, τ²=0.48). The network plot was shown in Fig. 8F. All PARP inhibitors showed statistically significant higher risk of dose reduction due to AE relative to placebo (Fig. 9F). According to the net league table, PARPi alone might decrease significantly the risk of AE leading to discontinuation compared with PARPi plus anti-angiogenic drug (OR, 0.10; 95% CI, 0.02–0.57) (Figure S9D). The P-scores showed a consistent ranking as the result of relative effects (Fig. 5B). For PARPi-based therapies, PARPi (0.422) had the lowest probability of experiencing dose reduction due to AE, while PARPi plus anti-angiogenic drug (0.013) had the highest probability. The cumulative probability plots (Figure S10D) and SUCRA scores (Figure S2B) showed similar results. Egger’s test (P = 0.410) suggested no publication bias, while funnel plot showed an unsymmetric distribution (Figure S8F). Sensitivity analyses were conducted considering the cancer type, sample size and trial phase. Only ovarian cancer subgroup decreased the heterogeneity significantly (Table S2).

Specific AE

Figure S11 showed the forest plots of specific AEs due to the 5 PARPi-based interventions. Fig. 5B showed the ranking profile based on the P-scores. Among all the 5 PARPi-based therapies, PARPi plus anti-angiogenic drug might have the highest risk for causing MDS/AML (0.340) and hypertension (0.061). PARPi plus ICI might have the highest risk for causing pneumonia (0.203) and cardiovascular event (0.153). All 5 PARPi-based therapies had similar risk for causing thromboembolic event. The cumulative probability plots (Figure S12) and SUCRA scores (Figure S2B) suggested consistent outcomes.

Discussion

Principal findings and contributions

In this study, we compared the AEs of different PARP inhibitors and PARPi-based combination therapies. Including 67 RCTs and 23,285 patients with solid tumors applying PARPi, our network meta-analysis demonstrated that olaparib, niraparib, rucaparib, fluzoparib, pamiparib, and senaparib all had higher risk of serious AE and AE-related discontinuation of treatment compared with placebo. However, senaparib was found to be related to a higher risk of serious AE compared with other PARPis and pamiparib was related to lower risk of serious AE. Senaparib also showed the highest risk for AE-related discontinuation of treatment, while niraparib showed the lowest risk among all 6 PARP inhibitors. Combining all the results referred above in our study, the safety of senaparib seemed to be poorer compared to other PARP inhibitors, and pamiparib showed the most ideal safety profile in summary.

As for the 5 PARPi-based therapies included in our study (PARPi, PARPi plus anti-angiogenic drug, PARPi plus ARSI, PARPi plus chemotherapy and PARPi plus ICI), they showed similar risk of serious AE. PARPi plus anti-angiogenic drug or chemotherapy might increase significantly the risk of AE-related discontinuation of treatment compared with PARPi alone. Of note, ARSI showed less toxicity compared with the 5 PARPi-based therapies. In summary, the safety of PARPi plus anti-angiogenic drug and PARPi plus chemotherapy was associated with a higher incidence of AEs.

As far as we know, this is the first study comparing the safety of various PARPi-based therapies. Moreover, this is the largest and most comprehensive network meta-analysis for the safety of PARPi in solid tumors.

Comparisons with other studies

Recently, several studies have validated the safety and tolerance of various types of PARPi in solid tumors [92, 93]. One previous study has proven the safety of four approved PARP inhibitors, including olaparib, talazoparib, niraparib and rucaparib. The safety profile of the four PARP inhibitors was comparable in terms of serious AE and AE-related discontinuation of treatment [12]. To quote talazoparib in the comparison, the control group of the previous study contained both placebo and chemotherapy. In our study, given the addition of more PARP inhibitors, we considered senaparib as the most toxic drug and pamiparib as the safest one. The advantage of our article comparing with the previous study was that the control arm excluded interference from other schemes because only trials using placebo alone were eligible. In addition, new PARP inhibitors, like fluzoparib and senaparib were included in our study to compare safety uniformly.

Sporadic but alarming signals of cardiac and vascular events have been reported, particularly in patients treated with niraparib due to its large spectrum of off-target effects [94]. Previous meta-analysis has reported that PARPi-based therapy is associated with a significantly increased risk of major adverse cardiovascular events and hypertension of any grade compared with controls [95, 96]. In our study, PARPi did not increase the risk of cardiovascular events compared with placebo. The reason for the inconsistent results might be the complex component of controlled groups in the previous study, while placebo was the only drug of controlled groups in our work. Nevertheless, PARPi was exactly proven to increase the risk of hypertension in this study and niraparib was the most toxic drug for hypertension as well.

Cancer-associated thrombosis is always a major health problem that affects morbidity and mortality of people with cancer. Surgical and systemic pharmacological anticancer treatments have a significant impact on the thrombotic risk of patients [97]. One previous meta-analysis did not demonstrate either a significant increase or reduction in the odds of thromboembolic events in patients diagnosed with metastatic solid tumors treated with PARPi [98], which was identical to our results. In addition, we supported that all the included PARPi-based therapies did not increase the risk for causing thromboembolic event significantly.

A meta-analysis including 18 RCTs published in 2021 reported PARPi increased the risk of MDS and AML versus placebo [99]. However, this PARPi group contained PARPi plus other treatment. In this way, confounding factors caused by other treatment might affect the final result. Our work comprising 16 RCTs showed no significant increase in risk of MDS/AML when using PARPi only, although PARPi had a trend to induce MDS/AML.

PARPi resistance and PARPi combinations

Similar to other chemotherapy agents, PARPi also face drug resistance. More than 40% of BRCA1/2-deficient patients failed to benefit from PARPi [15]. To enhance PARPi sensitivity and overcome PARPi resistance, several feasible strategies should be considered and implemented, including PARPi plus chemotherapy, PARPi plus ICI, PARPi plus targeted drugs, PARPi plus ARSI and PARPi plus anti-angiogenic drug. PARPi combinations have the potential to enhance efficacy through synergistic activity, and also potentially sensitize innately PARPi-resistant tumors to PARPi. Initial combinations involving PARPi with chemotherapy were hindered by significant overlapping hematologic toxicity [100]. Our study also demonstrated PARPi with chemotherapy induced the highest incidence of high-grade AEs and AE-related discontinuation of treatment. Antibody-drug conjugates (ADC) have been developed to deliver cytotoxic chemotherapy in a more targeted fashion to target-expressing cancer cells, which have the advantage of wider therapeutic windows and reduced toxicity [101], which is a promising drug when combined with PARPi.

The rational combination of antiangiogenics with PARPi have been explored in clinical studies, primarily in the setting of ovarian cancer [100]. The Phase 3 PAOLA-1 study evaluated the combination of PARPi and antiangiogenic treatment with bevacizumab as a first-line maintenance strategy after initial platinum-based chemotherapy in advanced ovarian cancer [102]. However, this combination regimen was proven to increase the incidence of serious AEs and hypertension in our study, which may leave hidden trouble for future application. Notably, PARPi with ICI significantly increased the risk of pneumonia and cardiovascular events, which may be caused by the immune related toxicity. The extensive use of PARPi with ICI still have to be validated by more clinical trials.

Clinical implications

The findings above might guide oncologists in selecting different PARPi-based therapies. Notably, although pamiparib was reported as the safest compound, it was less widely represented in clinical trials and practice compared with olaparib and niraparib. Given the limited number of the trials on senaparib and the findings of our work currently, senaparib might also be used carefully due to its higher risk of serious AEs. More RCTs on senaparib and pamiparib are urgently needed to evaluate its safety in future. Patients with hypertension or poor blood pressure control were not recommended to use niraparib, because of its high risk of hypertension. Patients with high Performance Status Score should avoid using PARPi combined with chemotherapy or antiangiogenic agent. In addition, patients with underlying heart disease should use PARPi combined with ICI with caution. Saruparib as a novel PARPi is still in the development stage, whose safety is unknown and needs further exploration [103].

Limitations

First, rare or delayed adverse events were not comprehensively captured during a short follow-up as in some RCTs, and could be affected by treatment duration and survival data. We had access only to the frequencies of adverse events that were made publicly available through peer-reviewed publications. Thus, we may have missed AEs which were not reported by the investigators and caused publication bias. Second, most of our analyses in our study had a relatively high heterogeneity. We have tried our best to find the source of the heterogeneity using sensitivity analysis. High heterogeneity was widely observed in both ovarian cancer and large population subgroups, driven by a multitude of complex population-based confounding factors. Trial phase did not induce the high heterogeneity. Besides, limited by inclusion criteria of some trials, patients with both first-line and back-line treatment were included, which disturbed the further analysis of heterogeneity. Thus, further clinical studies are needed to explore the impact of age, sex, treatment lines, etc. on risk of AEs to different PARP inhibitors. Third, our network did not form the necessary closed loop, it is necessary to honestly report that inconsistency testing could not be performed due to network structure reasons. Fourth, although pamiparib was considered to be the safest drug in our study, this result should be considered with caution because of limited trial numbers. Fifth, the definition of “serious AE” was different in each trial, which limited the accuracy of the comparison. Given the huge number of the included trials, this was inevitable. Finally, detailed clinical information such as BRCA1/2 status was unavailable because of no relevant content in original RCTs. Difference of AEs caused by specific gene mutation status were ambiguous, which deserved more exploration on this topic.

Conclusion

Our network meta-analysis indicated that pamiparib demonstrates the most favorable safety profile among evaluated PARP inhibitors relatively, while senaparib might be associated with a higher incidence of AEs. As for the 5 included PARPi-based therapies, PARPi plus anti-angiogenic drug or chemotherapy might cause the most AEs generally. Different PARPi and PARPi-based therapies are associated with unequal risk of specific AEs. Therefore, on the basis of balancing efficacy and safety, selecting optimal PARPi and formulating tailored combination therapy for individual patients is necessary.

Supplementary Information

Supplementary Material 1. (2.8MB, pdf)

Acknowledgements

Not applicable.

Authors’ contributions

Bangwei Cao and Chenyu Wei are expected to have made substantial contributions to the conception; Chenyu Wei, Shujing Liu and Jiexuan Hu have made substantial contributions to the acquisition, analysis, and interpretation of data; Chenyu Wei and Shujing Liu have drafted the work; Bing Liu has substantively revised it. All authors reviewed the manuscript.

Funding

National Natural Science Foundation of China [grant numbers 82173056].

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Bangwei Cao, Email: caobangwei@ccmu.edu.cn.

Bing Liu, Email: liubing19710820@163.com.

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Supplementary Materials

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Data Availability Statement

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