How well are Phase 2 cancer trial publications supported by preclinical efficacy evidence?

Preclinical assessment for translation to humans: The PATH approach for assessing supporting evidence for early-phase trials and innovative care

Early-phase trials and innovative care draw support from basic science, preclinical studies, and clinical research. Such evidential diversity presents a challenge for traditional ways of synthesizing evidence. In what follows, we review the limitations of existing approaches for communicating supporting evidence for early-phase trials. We then offer a structured approach, PATH (preclinical assessment for translation to humans). PATH is grounded in the premise that the case for administering novel strategies to patients requires connecting the dots between nine mechanistic steps supporting a clinical claim. Using PATH entails first parsing supporting evidence, assessing the strength of evidence at each step, and then assessing the strength of a chain of evidence linking drug administration to clinical effect. While PATH requires further refinement, the approach reduces some of the opacity, arbitrariness, and biases in current ways of presenting and assessing scientific support for early-phase trials and innovative care.

Probability of Regulatory Approval Over Time: A Cohort Study of Cancer Therapies

PURPOSE

New cancer therapies are frequently evaluated in multiple disease indications. We evaluated whether the probability of achieving US Food and Drug Administration (FDA) approval for a new cancer therapy changes with time.

METHODS

We identified a cohort of anticancer drugs with a first registered efficacy trial from 2007 to 2011 on ClinicalTrials.gov. We downloaded all clinical trials for each included drug from the initiation of efficacy testing to January 11, 2021. Each trial was categorized by cancer indication and assigned to investigational trajectories on the basis of unique drug-indication pairings. We performed a univariate Cox's proportional hazards regression to assess the probability of a trajectory leading to regulatory approval over time since initiation of the first efficacy trial for a given drug.

RESULTS

We included 213 drugs in our cohort, of which 37 (17.4%) received FDA approval in at least one oncology indication. In our primary analysis, we found a 15% decrease in the probability of approval for every year since initiation of the first efficacy trial (hazard ratio [HR], 0.85 [95% CI, 0.73 to 0.99]; P = .032). We found a 45% increase in the probability of approval for the first trajectory launched for a given drug in comparison with all others (HR, 0.55 [95% CI, 0.33 to 0.91]; P = .021).

CONCLUSION

Drug-indication pairings pursued years after initial testing for efficacy have lowered probability of affecting care. Clinical trial investigators, sponsors, and regulatory bodies may benefit from awareness of this trend when considering both early and late trajectory trials in a drug's development.

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

The Challenge of Combining Chemo- and Radiotherapy with Checkpoint Kinase Inhibitors

Preclinical models of cancer have demonstrated enhanced efficacy of cell-cycle checkpoint kinase inhibitors when used in combination with genotoxic agents. This combination therapy is predicted to be exquisitely toxic to cells with a deficient G1-S checkpoint or cells with a genetic predisposition leading to intrinsic DNA replication stress, as these cancer cells become fully dependent on the intra-S and G2-M checkpoints for DNA repair and cellular survival. Therefore, abolishing remaining cell-cycle checkpoints after damage leads to increased cell death in a tumor cell-specific fashion. However, the preclinical success of these drug combinations is not consistently replicated in clinical trials. Here, we provide a perspective on the translation of preclinical studies into rationally designed clinical studies. We will discuss successes and failures of current treatment combinations and drug regimens and provide a detailed overview of all clinical trials using ATR, CHK1, or WEE1 inhibitors in combination with genotoxic agents. This highlights the need for revised patient stratification and the use of appropriate pharmacodynamic biomarkers to improve the success rate of clinical trials.