Taking a drug from the laboratory and preclinical testing arena into first-in-human studies and clinical trials represents the largest, most costly gamble in the drug discovery pipeline.
During this phase of development patient safety takes center stage and acquires quantifiable parameters, dosing may require some guesswork and trial-and-error, unexpected toxicities and off-target effects may arise, and the first real sense of how a compound will be processed, metabolized, made available to tissues and cells, and affect normal physiology and disease becomes evident.
About one-third of experimental drugs never make it past Phase I trials, and only about 13% of those that enter clinical testing receive market approval. Nearly two-thirds of drugs that make it past Phase II fail in late-stage studies, following a substantial investment of time, dollars, and resources.
De-risking clinical development involves minimizing the guesswork and removing as many unknowns as possible as early as possible. In conventional drug discovery, de-risking strategies typically focus on accumulating as much knowledge about a compound as one can during the discovery and preclinical testing stages.
This typically includes defining and validating a compound’s mechanism of action, validating the role of the intended target in a pathological pathway or disease process, and understanding its potential off-target activity. However, even the most rigorous efforts to thoroughly characterize a compound’s bioavailability, pharmacokinetics, and mechanism of action in even the most robust assays and animal models cannot ensure success or eliminate risk in clinical development.
Proof-of-principle and absence of toxicity in preclinical models do not necessarily translate to efficacy and safety in humans. Most animal models only approximate a disease state in humans, and physiology and pharmacokinetics may differ substantially.
When possible, therefore, the best approach for minimizing risk in clinical development is to select a drug that has a well-documented mechanism of action clearly associated with a positive therapeutic effect in the disease of interest. Potential candidates may be experimental compounds developed for other diseases that failed along the path to regulatory approval, although shown to be safe and to have the intended activity, which can be repurposed for a new indication.
Existing drugs can be tried in different clinical settings or patient populations, administered using alternative delivery methods, or be reformulated, given together with another therapeutic or targeting agent, or be combined with a medical device to improve delivery or dosing.
Targeting Critical Care
The various de-risking scenarios and strategies described in this article are primarily relevant to conventional types of drug discovery approaches and therapeutic programs that target chronic diseases, such as diabetes, atherosclerosis, or hypertension.
In such cases, drug discovery typically begins with studies to understand disease etiology, identify a molecular target, and develop an intervention capable of correcting or compensating for the underlying disease process. Traditional drug discovery then proceeds along a well-established regulatory clinical design pathway culminating in at least three stages of clinical trials.
In contrast, in the critical-care arena, where there are often no approved treatments, little drug development activity, and high unmet need, and where patients are severely ill and in need of immediate, life-saving interventions rather than curative measures, there exist additional opportunities for de-risking clinical development.
The different goals of drug discovery for critical-care indications—namely treating the deranged physiology that contributes to acute organ failure—necessitate targeting physiologic processes rather than disease-related pathogenetic molecular targets.
This difference can expedite the selection of a clinical candidate and shorten the clinical development process. Well-tested therapies may already exist, whether on the market for other indications, in late-stage development, or abandoned in the portfolios of pharma companies.
Unlike untested clinical entities, they typically have well-understood physiological effects, pharmacokinetics, bioavailability, and dosing, and can undergo abbreviated preclinical testing to demonstrate a desired pharmacological effect and proceed directly to proof-of-concept in humans.
Due to the critically ill nature of these patients, the paucity of effective treatment options, and the relative rarity of acute organ failure (and, thus, small numbers of patients available for clinical studies), these are typically orphan indications with fast-track status. Often, only a single Phase IIb–III trial may be needed prior to applying for regulatory approval.
Accelerating Translation to the Bedside
In critical care, there are few approved therapies or firm standards for product (regulatory) approval, and the design and implementation of clinical trials presents unique challenges. These include the need to carry out trials in the intensive care setting, the short timelines for demonstrating efficacy, and the likelihood that patients will have co-morbidities and interrelated physiological abnormalities.
These challenges also create opportunities for applying innovative testing strategies and adaptive trial design that can accelerate the acquisition of meaningful clinical data.
Adaptive trial design is not uncommon in oncology to test novel therapies for severe, rapidly progressing tumors for which no other treatment options exist. Due to the nontraditional nature of adaptive trial design, it is important for companies to engage regulatory authorities early on and to maintain an ongoing dialogue throughout clinical development to ensure that the evidence to support product safety and therapeutic efficacy meets the requirements for market approval.
One strategy for adaptive trial design is the Bayesian approach, a “learn-as-you-go” method based on inference and probability theory and a prespecified, statistics-driven methodology. It involves defining up front how one will use the initial data acquired in a study to inform the analysis of subsequent trial results.
An example of a Bayesian approach to clinical trial design might involve the use of a surrogate biomarker as a measure of success toward achieving a desired therapeutic outcome. The biomarker may be subjective (such as a measure of how a patient feels) or may be as general as survival following a typically fatal clinical outcome.
Although the biomarker might not achieve a quantifiable level of clinical relevance, it allows one to make an assumption of clinical efficacy. Once a biomarker is identified, one can then design a pivotal trial to test the assumption, assessing the biomarker early on and then collecting data on clinically relevant endpoints. In essence, one defines the expected treatment effects during the course of the study.
Another form of adaptive trial design that can accelerate the path to market in critical care encompasses dose escalation in a single, pivotal efficacy trial. This eliminates the need for a separate dose-finding study. It can be used to evaluate the efficacy of a drug compound with a well-defined safe dosing range. A low dose of the drug is given to all patients in the treatment group, and the dose is increased in each patient as the trial progresses. Dosing effects are evaluated within individual patients.
Most repurposing of drugs outside the realm of critical care happens empirically, with serendipitous successes, such as sildenafil, being rare. In critical care, repurposing can be more of a targeted, premeditated strategy. Overall, the ability to enter clinical development with a well-defined chemical entity that already has proven its safety and utility in humans can minimize the risk of a costly late-stage failure, accelerate the path to market, and shift the once-unfavorable odds toward a more likely successful outcome.