Spero Therapeutics (SPRO) Total Liabilities (2016 - 2026)
Spero Therapeutics (SPRO) recorded Total Liabilities of $8.56 million in Q2 2026, down 70.8% from $29.29 million a year earlier but up 40.7% from the prior quarter.
Spero Therapeutics (SPRO) Total Liabilities (2016 - 2026) Analysis & Trends
At the end of FY2025, Spero Therapeutics reported Total Liabilities of $9.9 million, down 84.6% from FY2024.
- Annual Total Liabilities has a five-year compound annual growth rate of -14.3% (FY2020 to FY2025).
- Across earlier years, Total Liabilities came in at $64.42 million in FY2024 (-14.7%), $75.5 million in FY2023 (+54.5%), $48.87 million in FY2022 (-41.0%) and $82.78 million in FY2021 (+286.6%).
- Quarterly Total Liabilities has ranged from $6.09 million in Q1 2026 to $83.36 million in Q1 2022 over the past five years.
- On a year-over-year basis, Total Liabilities has declined for seven consecutive quarters, with an average decline of 47.8% over the last eight quarters.
- Peak year-over-year performance for Total Liabilities in the last five years was growth of 286.6% in Q4 2021, against a decline of 86.1% in Q1 2026 at the low end.
- Per Business Quant, the preceding three quarters came in at $6.09 million (Q1 2026), $9.9 million (Q4 2025) and $27.68 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Eli Lilly | 1,136.97 Bn | 1,105.55 Bn | 19.71 Bn | 108.40 Bn |
| 2 | Johnson & Johnson | 619.72 Bn | 538.25 Bn | 17.26 Bn | 116.09 Bn |
| 3 | AbbVie | 503.40 Bn | 476.52 Bn | 12.70 Bn | 141.05 Bn |
| 4 | Merck | 370.15 Bn | 324.53 Bn | 12.21 Bn | 87.82 Bn |
| 5 | Novartis Ag | 298.64 Bn | 262.63 Bn | 11.24 Bn | 80.09 Bn |
| 6 | Astrazeneca | 261.07 Bn | 234.76 Bn | 12.86 Bn | 65.46 Bn |
| 7 | Amgen | 237.00 Bn | 192.40 Bn | 7.24 Bn | 83.95 Bn |
| 8 | Gilead Sciences | 191.24 Bn | 165.43 Bn | 6.22 Bn | 37.53 Bn |
| 9 | Pfizer | 170.31 Bn | 116.96 Bn | 10.94 Bn | 115.64 Bn |
| 10 | Spero Therapeutics | 68.40 Mn | -127.38 Mn | - | 8.56 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 8.56 Mn |
| Mar 31, 2026 | 6.09 Mn |
| Dec 31, 2025 | 9.90 Mn |
| Sep 30, 2025 | 27.68 Mn |
| Jun 30, 2025 | 29.29 Mn |
| Mar 31, 2025 | 43.90 Mn |
| Dec 31, 2024 | 64.42 Mn |
| Sep 30, 2024 | 69.65 Mn |
| Jun 30, 2024 | 69.38 Mn |
| Mar 31, 2024 | 71.45 Mn |
| Dec 31, 2023 | 75.50 Mn |
| Sep 30, 2023 | 53.40 Mn |
| Jun 30, 2023 | 44.10 Mn |
| Mar 31, 2023 | 47.71 Mn |
| Dec 31, 2022 | 48.87 Mn |
| Sep 30, 2022 | 31.99 Mn |
| Jun 30, 2022 | 27.71 Mn |
| Mar 31, 2022 | 83.36 Mn |
| Dec 31, 2021 | 82.78 Mn |
| Sep 30, 2021 | 32.80 Mn |
Spero 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=SPRO&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "SPRO", "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=SPRO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();