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Pharma Agents · Cluster 1 of 5

Discovery & clinical trials

The pre-market end — structuring biomedical literature for discovery teams, flagging avoidable weaknesses in trial-protocol design, and speeding site feasibility — all working with non-regulated research data or anonymised metadata, never patient PII.

Use case 501

Pre-clinical literature & target synthesis

The pain point

Biomedical literature grows relentlessly, and R&D teams manually review thousands of papers, patents and lab logs to identify viable targets. The stakes are enormous: Deloitte puts the average cost of developing a drug at $2.23bn (rising to $2.67bn in its most recent analysis),51 against an industry-wide clinical failure rate of around 90%.52

Impact — organisation

Discovery teams bottlenecked on manual literature review; the risk of missing a relevant signal buried in unstructured text; slow, expensive early validation.

Impact — customer / client

For patients, ultimately, slower discovery of the therapies they need.

How it’s addressed

Operating purely in the non-regulated pre-clinical research phase, the agent digests unstructured literature and lab data, maps compound correlations and assembles a structured, fully-cited dossier for the discovery team — acting as a search-and-synthesis utility that always shows its sources, so scientists verify the raw data themselves before any candidate advances.

The benefits
Organisation

Designed to support faster, better-organised early target validation by structuring the literature — with full citations — so scientists spend their time on judgement rather than retrieval.

Customer / client

For patients, the prospect of accelerated discovery; for researchers, verifiable, sourced intelligence rather than a black box.

Use case 502

Clinical trial protocol design support

The pain point

Protocol amendments are a major, expensive source of delay. Tufts CSDD data shows 76% of Phase I–IV protocols now carry at least one amendment (up from 57% in 2015), averaging 3.3 each,53 with each substantial amendment costing roughly $141,000–$535,000 and adding around 260 days to the timeline.54 A meaningful share of amendments are avoidable design issues.

Impact — organisation

Six-figure change costs and months of delay per avoidable amendment; timelines extended by revalidation and ethics review; recurring design flaws that could have been caught earlier.

Impact — customer / client

Patients waiting longer for access to experimental treatments while protocols are reworked.

How it’s addressed

The agent evaluates past protocols, historical dropout patterns and comparator profiles to highlight operational vulnerabilities in a draft design — surfacing the risks for the clinical-operations director. It does not draft or finalise the protocol; it flags what an experienced reviewer should look at.

The benefits
Organisation

Designed to help teams spot avoidable design weaknesses before a protocol is finalised — supporting a reduction in the avoidable amendments that drive cost and delay, with the design decision remaining human.

Customer / client

For patients, potentially faster, less-disrupted access to well-designed trials.

Use case 503

Trial site feasibility & recruitment triage

The pain point

Patient recruitment is the primary bottleneck in clinical operations, responsible for the majority of trial delays: a systematic review and meta-analysis found that around 80% of trials fail to meet their initial enrolment target and timeline.56 Traditional site-feasibility studies rely on manual questionnaires to global investigative sites, producing fragmented data and poor screening accuracy.

Impact — organisation

Slow, manual site selection; fragmented feasibility data; the cost of launching sites that then under-perform.

Impact — customer / client

Patients in under-served regions missing the chance to take part, and trials slowed for everyone.

How it’s addressed

The agent parses unformatted site-capability data, equipment inventories and regional enrolment capacity to support faster, more consistent feasibility screening. It works only with anonymised institutional metadata and site-readiness metrics — it does not touch patient health records or live patient PII.

The benefits
Organisation

Designed to compress the site-selection phase and improve screening consistency, helping sponsors avoid non-performing sites — without ever handling patient personal data.

Customer / client

For principal investigators, less administrative load; for patients, better-matched, faster-starting trials.

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Sources & references

Statistics describe demand and conditions across the relevant sector and are drawn from the cited public sources. They characterise the sector-level problem these agents address; they are not performance claims for any DVAI product. Deep Voice AI Limited is a pre-revenue company and makes no representation as to outcomes for any individual organisation. Figures are current as at the date of the cited publications.

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