Resistance benchmark foundry for ADC biotechs to prove next-gen payloads beat TOPO-1 and microtubule ceilings.

1. A $1.1 billion upfront acquisition shows novel payload biology can create immediate M&A value, so biotech teams need faster evidence to justify their own payload bets.
2. Novartis and Fierce both frame resistance to TOPO-1 and microtubule payloads as a current limit of existing ADC classes, creating urgency around tools that measure resistance escape rather than generic potency.
3. The claim that an NMTi payload works in TOPO-1-resistant models makes resistant-context benchmarking a category-defining proof point, not an optional translational appendix.
4. Because the buyer acquired two lead assets plus a broader platform with multi-tumor potential, vendors that generate reusable payload evidence can sell into a portfolio workflow rather than a single experiment.

Catalyst. The Myricx deal turns payload-resistance proof from a scientific nice-to-have into a funded capital-allocation problem because activity in TOPO-1-resistant models is now valuable enough to drive a $1.1 billion upfront acquisition.

The startup pairs a curated bank of resistant solid-tumor models with a software workspace for head-to-head payload benchmarking. Customers submit candidate ADCs, linker-payload constructs, or comparator hypotheses, and the platform runs standardized assays against TOPO-1- and microtubule-resistant panels relevant to the target and tumor type. The output is not just raw potency data; it is a decision packet showing where the new payload regains kill, where resistance persists, and which tumor contexts deserve IND or licensing spend next. Teams can reuse the same benchmark across payload iterations, partner discussions, and diligence rooms instead of rebuilding resistant models from scratch each time. Over time, the company accumulates a proprietary map of payload-class performance by resistance context, target, and tumor biology.

What's different. CROs can run potency assays, academic groups can build one resistant model, and internal teams can compare one payload at a time, but none of them create a normalized, reusable dataset across payload classes and resistant tumor contexts. This startup wins by standardizing the comparator panel, assay design, and sponsor-facing decision packet around the exact question boards care about: does this payload beat TOPO-1 or microtubule resistance strongly enough to fund the next step? The moat grows as every benchmark campaign adds paired data linking payload mechanism, target, tumor context, and resistance escape.

Jobs to be done

flowchart LR
  Buyer[ADC translational lead] --> Pain[Cannot prove a new payload beats TOPO-1 resistance]
  Pain --> Product[ADC resistance benchmark platform]
  Product --> Outcome[Faster IND and partnering decisions]

Executive takeaways

• Myricx/Novartis makes payload-resistance proof economically salient: Novartis is paying $1.1B upfront for a platform explicitly pitched against TOPO-1 limits, and Pfizer's earlier Seagen deal shows ADC capability can move strategic capital at very large scale.[1][2][3]
• Resistance is not a niche translational footnote. Recent reviews and clinical papers show failure can arise from antigen loss, trafficking and lysosomal defects, payload biology, DNA-repair rewiring, and emergent TOP1 mutations, so generic potency studies are insufficient.[8][9][10][11][13][29]
• Budget already exists inside today's alternative stack: CROs and platform vendors market integrated ADC testing, resistant models, PDX, PD/PK, and manufacturing support, so a new entrant can sell against fragmented external spend rather than invent a net-new budget line.[15][16][17][18][24][25][27][28][34]
• The differentiated product surface is a neutral benchmark and evidence layer, not another wet-lab vendor. Incumbents can run pieces of the workflow, but no default vendor is synonymous with cross-sponsor, resistance-specific comparator evidence.[15][16][17][18][20][21][33]
• The immediate market is smaller than the broader ADC hype: the best customers are IND-near solid-tumor programs with novel payloads and near-term partner or board scrutiny, not every ADC program in discovery.[4][5][6][7][8][32]

Market definition

The relevant market is outsourced resistance benchmarking for ADC payload decisions: a translational evidence layer between generic oncology CRO execution and therapeutic asset ownership, focused on showing whether a novel payload class beats TOPO-1 or microtubule comparators in resistant solid-tumor models before IND or BD decisions.[7][8][9][10][11][15][16][17][18]

Customer and buyer

Daily users are ADC pharmacology, translational, and biomarker leads who must design comparator studies, choose model types, and answer diligence questions. The economic buyer is usually the CSO or head of translational/preclinical development because the spend competes with existing CRO, model, and development budgets rather than IT budgets.[15][16][17][18][24][28][34]

Buying triggers

• An IND-enabling, financing, or partnering event forces the team to prove that a novel payload does more than match legacy TOPO-1 or microtubule activity in standard screens. [1][2][11][13][29][32]
• Sequential ADC failure or ambiguous comparator data raises the question of whether resistance is target-related or payload-related, and the sponsor needs a clearer mechanism-grounded answer. [8][9][10][11][13][29]
• Too many one-off studies across CROs, academic labs, and internal models create a fragmented data room that leadership cannot defend confidently to partners or boards. [15][16][18][19][20][21][24][33][35]

Willingness to pay

Willingness to pay is credible because buyers already outsource ADC testing, translational pharmacology, resistant-model work, and broader development support. A resistance-benchmark product can map to pre-existing study budgets if it collapses multiple vendors into one decision-ready package and reduces reruns. [15][16][17][18][24][27][28][34]

Category dynamics

Growth signal 10.6%-18.7% CAGR in analyst reports for the broader ADC market

Validation signals

• Novartis explicitly paid $1.1B upfront for an NMTi ADC platform pitched against TOPO-1 limits, validating resistance escape as a premium claim.
• Pfizer's Seagen acquisition confirms that large pharma treats ADC platform capability as strategic, not peripheral.
• CROs and vendors are already marketing ADC-specific resistant models, integrated panels, and payload screens, indicating real buyer demand for externalized translational infrastructure.
• Daiichi Sankyo's B7-H3 ifinatamab deruxtecan priority review and DS-7300a preclinical data show the beachhead targets already support active clinical and preclinical competition.

Regulatory & technical constraints

• Sponsor-facing benchmark evidence has to fit within ICH S9 expectations for anticancer programs rather than look like exploratory biology alone.
• Electronic outputs intended for IND or diligence reuse need rigorous provenance and auditability under Part 11-style expectations.
• Public model programs emphasize molecular annotation, reproducibility, and comparative assessment across PDX, organoid, and cell-model types.
• Resistance findings are payload-class specific, so TOPO-1, microtubule, and newer payloads cannot be mixed casually without standardized comparator design.

Competition is substitute-heavy. CrownBio and Champions sell integrated translational execution; WuXi and Eurofins sell fuller ADC development stacks; Creative vendors sell bespoke evaluation services; and public model networks reduce the cost of assembling in-house alternatives. The whitespace is a neutral, resistance-specific benchmark dataset and evidence workspace rather than raw assay execution.[15][16][17][18][20][21][24][25][27][28][33][34]

Why incumbents do not win by default

• Integrated oncology CROs. CrownBio- and Champions-class vendors already sell model execution, but their center of gravity is study throughput rather than a neutral, cross-sponsor resistance dataset.
• Full-stack ADC development vendors. WuXi- and Eurofins-class vendors can own larger program scope, which helps distribution, but that same breadth makes them less obviously independent when a sponsor wants a board-ready benchmark on whether to kill or advance a payload.
• Boutique assay vendors. Creative-class vendors prove there is budget for bespoke ADC characterization, yet their model is still service-led rather than compounding into a standardized evidence asset.
• Public model networks and internal teams. PDXNet/NCI plus sponsor in-house work reduce the cost of assembling ingredients, but not the comparability, packaging, or cross-customer learning problem.

ADC biotechs moving novel payloads into IND-enabling work face a new bottleneck: proving that a payload escapes TOPO-1 or microtubule resistance strongly enough to justify tox, CMC, and partnering spend. The Myricx sale to Novartis turned resistant-context efficacy from nice translational data into capital-allocation evidence. The first customer is a 40-150 person Series A-C ADC biotech with a B7-H3 or HER2 solid-tumor program, a board or partner review inside 6-12 months, and no internal neutral benchmark capability. The product should start as a fixed resistance-benchmark package—curated resistant cell-line and organoid panels, standardized TOPO-1 and microtubule comparators, and an audit-ready decision pack—sold as a per-program SOW with optional workspace access. This wedge is narrow by design because it uses one buyer, one trigger, and one proof point: did the payload earn the right to advance or get killed before further IND or BD spend? Incumbent CROs already absorb related budget, so GTM should ride existing translational study spend and partner with labs rather than build a full-service CRO stack. The main strategic risk is not demand but trust: if standardized panels do not change sponsor decisions or cannot be reused credibly in diligence, the company collapses into bespoke services. Research-based sizing suggests an initial SAM around $42M and year-three SOM around $6.6M, but the exact live protocol count and the best first model mix remain unvalidated. If early pilots show sponsor-accepted decision packs, sub-six-week turnaround, and repeat spend from the same account, the company can expand from B7-H3/HER2 programs into broader ADC portfolio benchmarking.

Problem

• Sponsors can show generic potency, but they often cannot prove that a novel payload still works once TOPO-1 or microtubule payloads fail in resistant solid-tumor settings.
• The current evidence stack is fragmented across CRO panels, academic resistant models, public repositories, and internal comparator studies, so boards and partners see non-normalized data rather than a single decision-grade answer.
• IND, financing, and partnering milestones force CSOs to commit capital before they know whether resistance escape is real, which makes false- positive payload bets expensive.

Solution

• Run a standardized benchmark package against curated resistant cell-line and organoid panels for HER2 and B7-H3 programs, with fixed TOPO-1 and microtubule comparators and predefined readouts.
• Deliver an audit-ready evidence workspace and exported decision pack that shows where a payload regains activity, where resistance persists, and which models justify follow-on spend.
• Add optional partner-run PDX confirmation, retests, and multi-program benchmarking only after the fixed starter panel proves decision value.

Why we win

• Unlike integrated CROs, the company sells a neutral sponsor-facing benchmark layer whose product is the decision pack and reusable comparator history, not lab utilization.
• Starting with one narrow IND-near workflow lets the team standardize panels, turnaround, and pricing faster than a broad "all ADC services" entry would.
• Each paid benchmark adds a proprietary matrix of payload class, target, model, resistance mechanism, and sponsor outcome that becomes harder to recreate account by account.
• The company can partner with model banks, CROs, and biomarker labs instead of replacing them, which lowers go-to-market friction and keeps capex lighter.

Milestones

flowchart LR
  Wedge[IND-near ADC payload benchmark] --> MVP[Fixed resistant panels plus decision pack]
  MVP --> Proof[Sponsor-accepted go or no-go evidence]
  Proof --> Expansion[Repeat programs and portfolio subscriptions]

Founding team

Experiment roadmap

Risk assessment

What must be true

• At least 10-15 IND-near ADC programs per year fit the beachhead profile and have a live resistance-proof buying trigger.
• CSO or translational buyers will sign a $200k-$300k pilot before simply expanding scope with an incumbent CRO.
• A fixed starter panel plus optional confirmation work changes or validates sponsor go or no-go decisions in at least half of early pilots.
• At least 2 of the first 3 sponsor contracts allow blinded data retention and enough reuse to seed a benchmark graph.
• By month 18, at least half of revenue comes from repeat benchmarks or workspace subscriptions, proving the model is not one-off services.

Open diligence questions

• Which named HER2, B7-H3, or adjacent novel-payload programs have a board or partner milestone in the next 12 months?
• What exact comparator studies and budgets are those teams already buying from Crown, Champions, WuXi, or internal labs?
• Which first model mix actually changes CSO decisions most often: resistant cell lines, organoids, or PDX?
• What contractual language will sponsors accept for blinded data retention and cross-program benchmark reuse?
• How much assay setup and reporting can stay fixed before sponsor requests push gross margin below the 70% target?

Model sanity

• Revenue engine. Revenue is driven by active programs rising from 3 at Y1 exit to 11 at Y3 exit while blended program value matures from $200K-$300K pilots toward a $600K exit-rate active-program year.
• Must go right. The model needs a first paid pilot by Month 6 and same-buyer repeat work by Month 24 so ARPU expands without building a full-service CRO field team.
• Model breaks if. If sales cycles stretch toward eight months or PDX becomes default scope, the downside case falls to $3.9M Y3 revenue and cash turns slightly negative.
• Next-round proof. A credible seed story requires 6 active programs plus one working co-sell channel by Month 18 and the Month-24 proof that repeat or subscription revenue exceeds half of sales.

Scenarios

Sensitivity

flowchart LR
  TargetAccounts --> PaidPilots
  PaidPilots --> ActivePrograms
  ActivePrograms --> BenchmarkRevenue
  ActivePrograms --> RepeatWork
  RepeatWork --> BenchmarkRevenue
  BenchmarkRevenue --> GrossProfit
  GrossProfit --> Cash

Flags: The researched $6.6M SOM is best interpreted as Q4Y3 exit ARR; full-year Y3 revenue is $4.9M because the model does not reach 11 active programs until month 36. · The base case assumes more than half of month-24 revenue is repeat or subscription-driven; if accounts behave like one-off services, the 4% churn assumption is too low. · Gross margin only reaches 70% if partner-run PDX stays optional confirmation rather than standard in every benchmark SOW. · The customer count is really program count, so one delayed multi-program sponsor can move annual revenue by roughly $0.4M-$0.6M.

• Predictive validity gap. Resistant tumor models may not predict human clinical response well enough for sponsors to trust them in major go/no-go decisions. Mitigation: Start with decision-support benchmark packages, include known comparator controls, and build retrospective concordance data before claiming the platform can predict trial outcomes.
• Services creep. Too much bespoke wet-lab work could make the company look like a high-touch CRO instead of a scalable product business. Mitigation: Standardize a small set of resistant panels, assay templates, and sponsor-ready outputs, then attach a recurring software workspace and dataset subscription to each campaign.
• Narrow initial buyer base. The number of ADC teams with both a novel payload and near-term IND or partnering pressure is limited in the early market. Mitigation: Target the highest-urgency venture-backed ADC programs first, then expand into mid-size pharma portfolio reviews and adjacent targeted-conjugate modalities once the benchmark dataset is trusted.