Monte Rosa Therapeutics (GLUE) Total Liabilities (2020 - 2026)
Monte Rosa Therapeutics (GLUE) recorded Total Liabilities of $203.88 million in Q2 2026, up 122.8% from $91.51 million a year earlier but down 5.0% from the prior quarter.
Monte Rosa Therapeutics (GLUE) Total Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, Monte Rosa Therapeutics reported Total Liabilities of $215.6 million, down 0.1% from FY2024.
- Annual Total Liabilities has a five-year compound annual growth rate of 17.1% (FY2020 to FY2025).
- Across earlier years, Total Liabilities came in at $215.8 million in FY2024 (+73.3%), $124.51 million in FY2023 (+75.4%), $70.98 million in FY2022 (+277.3%) and $18.81 million in FY2021 (-80.8%).
- Quarterly Total Liabilities has ranged from $14.74 million in Q3 2021 to $215.8 million in Q4 2024 over the past five years.
- On a year-over-year basis, Total Liabilities rose in six of the last eight quarters, with growth averaging 51.9%.
- Peak year-over-year performance for Total Liabilities in the last five years was growth of 413.8% in Q2 2022, against a decline of 80.8% in Q4 2021 at the low end.
- Per Business Quant, the preceding three quarters came in at $214.51 million (Q1 2026), $215.6 million (Q4 2025) and $214 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Johnson & Johnson | 655.56 Bn | 574.09 Bn | 17.26 Bn | 116.09 Bn |
| 2 | AbbVie | 470.94 Bn | 444.10 Bn | 12.70 Bn | 141.05 Bn |
| 3 | Merck | 367.14 Bn | 321.57 Bn | 12.21 Bn | 87.82 Bn |
| 4 | Novartis Ag | 280.33 Bn | 236.20 Bn | 11.24 Bn | 80.09 Bn |
| 5 | Astrazeneca | 257.78 Bn | 231.34 Bn | 12.86 Bn | 65.46 Bn |
| 6 | Amgen | 226.04 Bn | 181.44 Bn | 7.24 Bn | 83.95 Bn |
| 7 | Gilead Sciences | 188.89 Bn | 163.01 Bn | 6.22 Bn | 37.53 Bn |
| 8 | Pfizer | 163.75 Bn | 110.70 Bn | 10.94 Bn | 115.64 Bn |
| 9 | Vertex Pharmaceuticals | 133.68 Bn | 105.69 Bn | 2.84 Bn | 7.18 Bn |
| 10 | Monte Rosa Therapeutics | 998.00 Mn | 409.81 Mn | - | 203.88 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 203.88 Mn |
| Mar 31, 2026 | 214.51 Mn |
| Dec 31, 2025 | 215.60 Mn |
| Sep 30, 2025 | 214.00 Mn |
| Jun 30, 2025 | 91.51 Mn |
| Mar 31, 2025 | 118.04 Mn |
| Dec 31, 2024 | 215.80 Mn |
| Sep 30, 2024 | 109.62 Mn |
| Jun 30, 2024 | 119.44 Mn |
| Mar 31, 2024 | 114.23 Mn |
| Dec 31, 2023 | 124.51 Mn |
| Sep 30, 2023 | 67.58 Mn |
| Jun 30, 2023 | 64.96 Mn |
| Mar 31, 2023 | 65.30 Mn |
| Dec 31, 2022 | 70.98 Mn |
| Sep 30, 2022 | 66.51 Mn |
| Jun 30, 2022 | 63.01 Mn |
| Mar 31, 2022 | 20.13 Mn |
| Dec 31, 2021 | 18.81 Mn |
| Sep 30, 2021 | 14.74 Mn |
Monte Rosa Therapeutics Total Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-liabilities&ticker=GLUE&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "GLUE", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=GLUE&period=max&api_key=YOUR_API_KEY");
const data = await res.json();