> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pav.bio/llms.txt
> Use this file to discover all available pages before exploring further.

# Screen deals for comparables

> Find comparable licensing and collaboration deals, rank them by stated economics, and trace each term to its source.

This guide screens antibody-drug conjugate (ADC) licensing and collaboration
deals announced since 2024, ranks them by stated upfront payment, and opens
the top deal for its full terms and source filings.

## Prerequisites

* A Pav API key in `PAV_API_KEY`. See [Quickstart](/quickstart).
* Python 3.9+ and `pip install requests`.

## Approach

1. **Search every phrasing.** Deal `q` matches exact terms with no synonym
   expansion, so search `antibody-drug conjugate` and `ADC` separately and
   merge by `deal_id`.
2. **Filter by type and date** with `deal_type` and `announced_from`.
3. **Rank by a stated term.** List rows carry `total_value`, `upfront` and
   milestone values. Many deals disclose no figure; skip those rather than
   treating them as zero.
4. **Open the deal** with `GET /v1/deals/{deal_id}` for royalties,
   territories, rights and the source of each lifecycle event.

## Script

```python deal_screen.py theme={null}
import os

import requests

API = "https://api.pav.bio"
HEADERS = {"Authorization": f"Bearer {os.environ['PAV_API_KEY']}"}


def get(path, **params):
    resp = requests.get(f"{API}{path}", headers=HEADERS, params=params, timeout=60)
    resp.raise_for_status()
    return resp.json()


# 1. Search each phrasing: deal search matches exact terms, with no synonyms.
deals = {}
for phrase in ["antibody-drug conjugate", "ADC"]:
    offset = 0
    while True:
        page = get(
            "/v1/deals",
            q=phrase,
            deal_type=["licensing", "collaboration"],
            announced_from="2024-01-01",
            limit=100,
            offset=offset,
        )["data"]
        for d in page:
            deals[d["deal_id"]] = d
        if len(page) < 100:
            break
        offset += 100


# 2. Keep deals with a stated upfront, largest first.
def amount(value):
    return (value or {}).get("amount") or 0


priced = [d for d in deals.values() if amount(d.get("upfront"))]
priced.sort(key=lambda d: amount(d.get("upfront")), reverse=True)
print(f"{len(deals)} ADC licensing/collaboration deals since 2024; {len(priced)} state an upfront")
for d in priced[:5]:
    total = (d.get("total_value") or {}).get("raw_text", "total not stated")
    print(f"{d['announced_at'][:10]} | {d['deal_name'][:70]} | upfront: {d['upfront']['raw_text']} | total: {total}")

# 3. Open the top deal for its full terms and sources.
top = get(f"/v1/deals/{priced[0]['deal_id']}")
print()
print(top["deal_name"])
for key in ["upfront", "milestones", "royalties"]:
    term = top.get("terms", {}).get(key)
    if term:
        print(f"  {key}: {term['raw_text']}")
for event in top.get("events", [])[:3]:
    print(f"  {event['event_type']}: {event.get('source_url')}")
```

```text Example output (September 2026) theme={null}
8 ADC licensing/collaboration deals since 2024; 4 state an upfront
2024-12-20 | Exclusive license for three-asset ADC portfolio | upfront: $44M in upfront payments | total: total not stated
2024-01-08 | Exclusive worldwide license of ZPC-21 and Zentalis’ ADC platform techn | upfront: up-front payment of $35 million in cash and Immunome common stock | total: total not stated
2026-04-09 | C4 Therapeutics and Roche enter DAC research and development collabora | upfront: an upfront payment of $20 million | total: total not stated
2025-10-08 | Lisata and Catalent worldwide non-exclusive certepetide license agreem | upfront: $70 thousand in connection with an upfront license fee | total: total not stated

Exclusive license for three-asset ADC portfolio
  upfront: $44M in upfront payments
  milestones: cumulative development milestone payments of up to $265 million, cumulative commercial milestone payments of up to $540 million
  royalties: single-digit royalties
  announced: https://www.sec.gov/Archives/edgar/data/1422142/000119312524282407/d891246dex991.htm
```

Exact-term search can also match deals that mention the term in passing. Read
`summary` and `rights_summary` before treating a hit as a comparable.

## Reading deal terms

* `amount` is in base currency units with an ISO `currency`. `raw_text` keeps
  the source wording, including qualifiers such as "up to".
* `not_disclosed: true` means the source explicitly withheld the term. A
  missing term means none was stated.
* `evidence` is the source sentence each value was read from; each lifecycle
  event carries the `source_url` that reported it.
* `latest_event_type` is the deal's current state. Filter with
  `event_type=completed` for closed deals, or `terminated` for failed ones.

## Variations

* **One company's deal history:** `company_id=<id>` with `sort=announced_at`.
* **Large M\&A:** `deal_type=acquisition&min_value_usd=1000000000&sort=headline_value`.
* **Exact count for a screen:** add `include_total=true`.