The Medicines We Never Get: Why Promising Science Fails to Reach Patients Clarus Biotech Review Follow the decisions between a promising result and completed care—and distinguish scientific failure from avoidable execution gaps. The laboratory result is convincing enough to justify another experiment. The team can describe the mechanism. Investors can imagine a market. Years later, the patient still has no new treatment. What happened in between? There is no single answer—and no sound basis for assuming that every discontinued program would have become a useful medicine. Many should stop because the evidence does not support further exposure or investment. But the path also contains failures of coordination, product readiness, and access that deserve separate scrutiny. The first loss: mistaking a signal for a medicine A biological finding and a clinically useful product answer different questions. A research team may demonstrate an effect in a model while still needing to establish whether that effect survives a different experiment, whether a tolerable exposure can be achieved, and whether it changes an outcome people value. The 2011–2020 BIO, Informa, and QLS analysis estimated a 7.9% likelihood of approval from Phase I across its development dataset; the Phase II transition rate was 28.9%. These are historical cohort estimates, not a forecast for an individual asset. They also do not tell us what fraction of failures was preventable. [1] The management implication is not to make every program look safer. It is to specify what evidence would justify the next commitment—and what result would justify stopping. A clear negative answer can preserve resources for a stronger idea. An ambiguous answer followed by a larger trial can consume both money and the opportunity to learn. The second loss: a trial that cannot answer the decision Imagine a fictional development team that describes its next milestone simply as “complete Phase II.” Before committing, it should ask: in which patients, against what comparison, on which endpoint, and with what follow-up? A trial can finish on schedule without resolving the question that a regulator, partner, clinician, or payer needs answered. Recruitment deserves the same discipline. A large epidemiological population is not an enrollment forecast. Sites must identify people who meet the criteria, approach them appropriately, support participation, and retain them. The operating plan should make those steps visible before a recruitment shortfall becomes a financing emergency. For this fictional team, one useful meeting would bring clinical, regulatory, patient-engagement, manufacturing, and commercial leaders around the same protocol. Their task would be to identify conflicting assumptions while revisions are still feasible. The third loss: time consumes the options Consider an invented company with $36 million in cash and a constant $3 million monthly baseline burn. It has 12 months of baseline runway before additional costs or financing. A six-month delay consumes $18 million at that burn rate. It does not establish an $18 million incremental loss without specifying the original plan, and it excludes any extra trial or manufacturing expenditure. The point is the relationship between learning and liquidity. If the next interpretable readout arrives after the company runs out of cash, a scientifically reasonable plan can become operationally impossible. Financing, partnering, and staged investment belong in milestone design because they determine whether the next question can be answered. An early partnership may preserve the experiment while reducing retained economics. Raising more capital may preserve ownership but create dilution and execution obligations. Neither choice is universally better; the assumptions should be visible enough for a board to compare them. The fourth loss: the product cannot reliably reach the clinic Approval and availability are separate achievements. FDA’s drug-shortage work identified weak economic incentives, insufficient rewards for mature quality systems, and barriers to recovery after disruptions. That report concerns shortages, not a direct estimate of why experimental medicines fail. Its relevance is the reminder that supply requires an operating and economic system as well as a validated product. [2] For a developing therapy, the corresponding review asks who owns process changes, which assays support release, what capacity is actually available, and how long a backup would take to qualify. A second supplier on a slide is not equivalent to a usable second source. The fifth loss: access exists only in the forecast A treatment can be clinically relevant and still require more coordination than a patient or care center can sustain. Coverage, referral, scheduling, travel, caregiver time, and monitoring can each constrain delivery. The CMS Cell and Gene Therapy Access Model illustrates an organized attempt to connect innovative therapies with payment and access, initially for sickle cell disease. Its existence is a reason to study implementation, not to presume success. [3] The patient’s question is not whether the launch occurred. It is whether appropriate care can be completed. A launch plan should therefore connect the evidence narrative to the actual sequence of decisions and appointments. What would have to be true? The program would need a reproducible scientific foundation and a defined benefit–risk question. The next study would need to resolve a decision rather than merely produce activity. Cash, manufacturing, and recruitment would need to last through an interpretable milestone. Supply and care pathways would need accountable owners and measurable readiness. Stopping rules would need to protect patients and capital when the evidence changes. A better way to review the lost opportunity Separate four possibilities: the biology failed; the evidence remained inconclusive; the organization could not execute; or an effective product could not be delivered sustainably. More than one may apply. Retrospective analysis should show what was known at the time, what alternatives were feasible, and where uncertainty remains. Value Inflection Engineering begins with that separation. It cannot guarantee a successful medicine. It can make the next decision, its evidence requirements, and its operating dependencies explicit—so that a promising idea receives a fair test and an unpromising one does not survive on narrative alone. Sources https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf https://www.fda.gov/drugs/drug-shortages/report-drug-shortages-root-causes-and-potential-solutions https://www.cms.gov/priorities/innovation/innovation-models/cgt Original article: https://www.clarusreview.bio/articles/the-medicines-we-never-get