Foghorn Therapeutics (FHTX) Operating Expenses (2019 - 2026)
Foghorn Therapeutics (FHTX) reported Operating Expenses of $24.88 million for Q2 2026, down 13.2% from $28.65 million a year earlier but up 0.1% from the prior quarter.
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Analysis
Foghorn Therapeutics (FHTX) Operating Expenses (2019 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Foghorn Therapeutics' Operating Expenses came in at $109.5 million, down 5.6% year-over-year; for FY2025, it was $117.3 million, down 6.4% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 11.2% (FY2020 to FY2025).
- By year, Operating Expenses came in at $125.29 million in FY2024 (-11.8%), $142.06 million in FY2023 (+4.2%), $136.37 million in FY2022 (+33.6%) and $102.05 million in FY2021 (+48.0%).
- Five-year quarterly Operating Expenses spans a low of $24.84 million in Q1 2026 and a high of $38.63 million in Q1 2023.
- Year over year, Operating Expenses gained in 1 of the last eight quarters, with an average decline of 8.7%.
- The high point for year-over-year Operating Expenses in five years was Q2 2022 (growth of 43.1%); the low point was Q3 2025 (a decline of 15.8%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $24.84 million (Q1 2026), $33.13 million (Q4 2025) and $26.65 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Eli Lilly | 1,348.13 Bn | 1,321.74 Bn | 20.46 Bn | 15.99 Bn |
| 2 | Johnson & Johnson | 675.90 Bn | 595.76 Bn | 17.30 Bn | 11.12 Bn |
| 3 | AbbVie | 519.45 Bn | 487.50 Bn | 13.12 Bn | 11.14 Bn |
| 4 | Merck | 364.95 Bn | 327.95 Bn | 13.19 Bn | 12.53 Bn |
| 5 | Novartis Ag | 311.58 Bn | 285.16 Bn | - | - |
| 6 | Astrazeneca | 289.34 Bn | 262.94 Bn | 13.26 Bn | 9.00 Bn |
| 7 | Amgen | 251.64 Bn | 203.30 Bn | 7.58 Bn | 6.66 Bn |
| 8 | Gilead Sciences | 212.05 Bn | 187.07 Bn | 6.73 Bn | 4.84 Bn |
| 9 | Pfizer | 167.35 Bn | 115.00 Bn | 12.56 Bn | 7.09 Bn |
| 10 | Foghorn Therapeutics | 113.93 Mn | -202.99 Mn | - | 26.90 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 26.90 Mn |
| Jun 30, 2026 | 24.88 Mn |
| Mar 31, 2026 | 24.84 Mn |
| Dec 31, 2025 | 33.13 Mn |
| Sep 30, 2025 | 26.65 Mn |
| Jun 30, 2025 | 28.65 Mn |
| Mar 31, 2025 | 28.87 Mn |
| Dec 31, 2024 | 26.86 Mn |
| Sep 30, 2024 | 31.66 Mn |
| Jun 30, 2024 | 33.52 Mn |
| Mar 31, 2024 | 33.24 Mn |
| Dec 31, 2023 | 31.23 Mn |
| Sep 30, 2023 | 34.56 Mn |
| Jun 30, 2023 | 37.65 Mn |
| Mar 31, 2023 | 38.63 Mn |
| Dec 31, 2022 | 36.07 Mn |
| Sep 30, 2022 | 34.89 Mn |
| Jun 30, 2022 | 33.68 Mn |
| Mar 31, 2022 | 31.72 Mn |
| Dec 31, 2021 | 28.80 Mn |
API Access
Foghorn Therapeutics Operating Expenses 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=operating-expenses&ticker=FHTX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "FHTX", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=FHTX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();