Neurocrine Biosciences (NBIX) Operating Expenses (2009 - 2026)
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Analysis
Neurocrine Biosciences (NBIX) Operating Expenses (2009 - 2026) Analysis & Trends
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 | Neurocrine Biosciences | 17.13 Bn | 13.21 Bn | 1.10 Bn | 791.40 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 791.40 Mn |
| Jun 30, 2026 | 807.50 Mn |
| Mar 31, 2026 | 621.10 Mn |
| Dec 31, 2025 | 594.60 Mn |
| Sep 30, 2025 | 555.90 Mn |
| Jun 30, 2025 | 541.90 Mn |
| Mar 31, 2025 | 549.00 Mn |
| Dec 31, 2024 | 485.70 Mn |
| Sep 30, 2024 | 438.30 Mn |
| Jun 30, 2024 | 444.80 Mn |
| Mar 31, 2024 | 416.00 Mn |
| Dec 31, 2023 | 364.90 Mn |
| Sep 30, 2023 | 357.60 Mn |
| Jun 30, 2023 | 379.10 Mn |
| Mar 31, 2023 | 534.60 Mn |
| Dec 31, 2022 | 308.60 Mn |
| Sep 30, 2022 | 300.10 Mn |
| Jun 30, 2022 | 323.50 Mn |
| Mar 31, 2022 | 307.50 Mn |
| Dec 31, 2021 | 348.30 Mn |
API Access
Neurocrine Biosciences 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=NBIX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "NBIX", "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=NBIX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();