DecisionX for Pharma R&D

Decision AI for
Pharma R&D

From first target to patent cliff, a drug moves through six stages, and at every one, a decision either gets made well and fast, or it gets made late, wrong, and expensive. Every stage runs on a clock; one of them never stops. This is where DecisionX plugs in at each stage.

Pharma R&D decision lifecycle

Six stages. Every one on a clock.

Six stages from Discovery to Lifecycle management, each shown as a gauge indicating relative severity of cost-of-delay, with Post-approval marked as perpetual.

01DISC
Discovery
$2–3B
at risk if wrong target
02PRE
Preclinical
$50–100M+
sunk if signal missed
03CLIN
Clinical trials
$500K/day
validated · highest $/day
04REG
Regulatory filing
6–18 mo
delay if rejected
Post-approval
Forever
validated · perpetual clock
06LIFE
Lifecycle
70–90%
erosion in yr 1 post-cliff
Gauge fill = relative severity of cost-of-delay Teal = validated from engagement data Amber = industry benchmark

Stage by stage

Where DecisionX plugs in,
at every stage of the lifecycle.

Each stage below shows the decisions made, the data they run on, what DecisionX solves, and what it costs to get them wrong or make them late.

DiscoveryProposed · benchmark

Before a molecule exists, someone chooses which biological bets are even worth pursuing: the highest-leverage, least-evidenced decision in the entire lifecycle.

Decisions

  • Target identification & validation
  • Hit-to-lead triage
  • Lead optimization trade-offs (potency vs. selectivity vs. ADMET)
  • Discovery-portfolio prioritization

Data sources

  • HTS assay results
  • Genomic / proteomic databases
  • In-silico ADMET predictions
  • Literature & patents
  • Competitive intelligence
  • Historical program success/failure rates

What DecisionX solves

Ranks targets and leads against multiple weak, noisy signals at portfolio scale, forecasting probability of success per candidate against historical base rates instead of the loudest champion's confidence.

Cost of being wrong

Average cost to bring one drug to approval is commonly cited near $2–3B; a wrong target choice means every downstream dollar, preclinical and clinical, is spent on a candidate that was never going to work.

Industry benchmark

Cost of being slow

The patent clock often starts during discovery/preclinical filing, so slow triage quietly shortens the commercial exclusivity window before a single patient is dosed.

Industry benchmark
PreclinicalProposed · benchmark

The last checkpoint before human exposure: where a safety signal either gets caught on cheap animal data or surfaces later on expensive human data.

Decisions

  • IND-enabling safety go/no-go
  • Species / model selection
  • Formulation & CMC readiness
  • Dose range-finding

Data sources

  • GLP toxicology studies
  • PK/PD data
  • Animal model results
  • Safety pharmacology panels
  • CMC / manufacturing data
  • Regulatory guidance

What DecisionX solves

Diagnoses whether a preclinical safety signal is real or a model artifact, adjudicates the go/no-go decision with a documented evidence trail, and simulates human dose projection from animal data.

Cost of being wrong

A missed safety signal that only surfaces in the clinic can kill a program after $50–100M+ of trial spend is already sunk, or worse, becomes a patient-safety event. A false-positive kill discards a potentially viable asset.

Industry benchmark

Cost of being slow

Preclinical typically runs one to three years; every added quarter here is a quarter off the eventual commercial exclusivity runway, and a gift to whichever competitor reaches the clinic first.

Industry benchmark
Clinical trialsValidated · sourced

The stage with the sharpest, most measurable clock in the entire lifecycle, where DecisionX's current pharma engagement evidence is concentrated.

Decisions

  • Enrollment & site-timeline commitment
  • Trial site selection
  • Protocol design & optimization
  • Digital-twin stress-testing & contingencies
  • Phase-transition go/no-go

Data sources

  • Site enrollment history
  • EHR / claims data
  • CRO reports
  • Protocol / amendment histories
  • Competitor trial registries
  • Real-world data
  • Biomarker / lab data

What DecisionX solves

Reconciles inconsistent site-history definitions, forecasts per-site enrollment curves, flags amendment-risk design elements before lock, and simulates trial dynamics with pre-committed contingency triggers.

Cost of being wrong

Roughly half of kill/continue decisions are made after the statistically optimal point; a failed Phase III alone commonly runs into the hundreds of millions to over $1B in sunk cost.

Sourced

Cost of being slow

Approximately $500K per day of trial delay, the highest dollar-per-day figure anywhere in the pipeline, because every day compounds against a fixed patent clock and competitive window.

Sourced
Regulatory filingProposed · benchmark

The narrowest gate in the lifecycle: a single verdict from a single reviewer body that either opens the market or costs a year.

Decisions

  • Submission-readiness adjudication
  • Filing-sequence prioritization across markets
  • Response strategy to regulator queries

Data sources

  • Regulatory guidance & precedent decisions
  • Prior submission histories
  • Agency correspondence
  • Clinical study reports
  • Statistical analysis plans
  • Quality / manufacturing data

What DecisionX solves

Diagnoses which dossier elements historically trigger delays or rejections at each regulator, adjudicates first-cycle-approval readiness before submission, and prioritizes market-filing order under finite regulatory-affairs capacity.

Cost of being wrong

A rejection or Complete Response Letter can delay launch six to eighteen-plus months and cost additional millions to resubmit, while competitors use the window to capture share.

Industry benchmark

Cost of being slow

Every month of filing delay is a month of exclusive-market revenue that is permanently lost, not merely deferred: for a blockbuster asset, that can be $50–100M+ per month.

Industry benchmark
Post-approvalValidated · sourced

The one stage with no finish line: every case, forever, on a clock that resets with each new report and never fully stops.

Decisions

  • Adverse-event case adjudication (causality + reportability)
  • Aggregate safety signal detection
  • Label-update triggers

Data sources

  • Spontaneous reports (literature, patient programs, call centers, social media, B2B partners)
  • Real-world / claims data
  • Clinical follow-up data
  • Regulatory case databases

What DecisionX solves

Extracts case facts from eight unstructured, multilingual source types, scores causality across drug classes, and delivers a near-touchless verdict, pulling humans in only on flagged exceptions.

Cost of being wrong

Manual extraction runs roughly 60% accurate with human error at 20–30%; a missed real signal risks patient harm and, at the extreme, product withdrawal, among the highest reputational and legal costs in the lifecycle.

Sourced

Cost of being slow

A missed 15-day regulatory deadline is a compliance violation on its own, per case, forever: not a one-time risk but a perpetual operating exposure across the entire portfolio.

Sourced
Lifecycle managementProposed · benchmark

The stage where the calendar decides for you: patent cliffs don't wait for a decision to be ready.

Decisions

  • Indication-expansion prioritization
  • Patent-cliff / generic-defense strategy
  • RWE-driven label expansion
  • Discontinuation calls

Data sources

  • Real-world evidence / claims data
  • Competitive & generic-entry intelligence
  • Patent expiry calendars
  • Sales / market data
  • Ongoing post-market studies

What DecisionX solves

Prioritizes which indications or label expansions to pursue under a finite medical-affairs and R&D budget, forecasts revenue-at-risk against the patent-expiry calendar, and surfaces evidence gaps before a competitor files first.

Cost of being wrong

Losing an indication-expansion race to a competitor can hand away hundreds of millions in incremental revenue in an adjacent patient population.

Industry benchmark

Cost of being slow

Patent cliffs are fixed dates, not moving targets: generic erosion commonly claims 70–90% of sales within the first year post-exclusivity, so every quarter of delayed defense is revenue that cannot be recovered later.

Industry benchmark

The System

Why this needs a decision system,
not a dashboard.

Causality

Why enrollment stalled, a case is causal, or a protocol needs to change.

Clinical trials + post-approval: built on diagnosis, not correlation

Self-learning

Every site ranking and ICSR verdict sharpens the next cycle.

+14–15 pts on deviation & timeline reduction, once it compounds

Unified context

CRO, EDC, and eight spontaneous-report sources reconciled first.

One trusted case file before enrollment or ICSR verdicts run

Decision AI for Pharma R&D

Every stage runs on a clock.
Decide before it costs you.

See DecisionX on your trials, your safety cases, your pipeline. First value in 15 days.