Implementing precision medicine initiatives in the clinic: a new paradigm in drug development

Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

PURPOSE

Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development.

METHODS

PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW).

RESULTS

We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2).

CONCLUSION

As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

Development of Molecularly Driven Targeted Combination Strategies

Targeted therapies are often inadequate when used in isolation as single agents. Combining diverse targeted therapies could optimally impact patient care by overcoming cancer resistance mechanisms. This commentary highlights such combinatorial strategies, including discussion on a recent trial.

Current Challenges in Cancer Treatment

PURPOSE

In this review, we highlight the current concepts and discuss some of the current challenges and future prospects in cancer therapy. We frequently use the example of lung cancer.

METHODS

We conducted a nonsystematic PubMed search, selecting the most comprehensive and relevant research articles, clinical trials, translational papers, and review articles on precision oncology and immuno-oncology. Papers were prioritized and selected based on their originality and potential clinical applicability.

FINDINGS

Two major revolutions have changed cancer treatment paradigms in the past few years: targeting actionable alterations in oncogene-driven cancers and immuno-oncology. Important challenges are still ongoing in both fields of cancer therapy. On the one hand, druggable genomic alterations are diverse and represent only small subsets of patients in certain tumor types, which limits testing their clinical impact in biomarker-driven clinical trials. Next-generation sequencing technologies are increasingly being implemented for molecular prescreening in clinical research, but issues regarding clinical interpretation of large genomic data make their wide clinical use difficult. Further, dealing with tumor heterogeneity and acquired resistance is probably the main limitation for the success of precision oncology. On the other hand, long-term survival benefits with immune checkpoint inhibitors (anti-programmed death cell protein-1/programmed death cell ligand-1[PD-1/L1] and anti-cytotoxic T lymphocyte antigen-4 monoclonal antibodies) are restricted to a minority of patients, and no predictive markers are yet robustly validated that could help us recognize these subsets and optimize treatment delivery and selection. To achieve long-term survival benefits, drug combinations targeting several molecular alterations or cancer hallmarks might be needed. This will probably be one of the most challenging but promising precision cancer treatment strategies in the future.

IMPLICATIONS

Targeting single molecular abnormalities or cancer pathways has achieved good clinical responses that have modestly affected survival in some cancers. However, this approach to cancer treatment is still reductionist, and many challenges need to be met to improve treatment outcomes with our patients.

Immune cell profiling to guide therapeutic decisions in rheumatic diseases

Biomarkers are needed to guide treatment decisions for patients with rheumatic diseases. Although the phenotypic and functional analysis of immune cells is an appealing strategy for understanding immune-mediated disease processes, immune cell profiling currently has no role in clinical rheumatology. New technologies, including mass cytometry, gene expression profiling by RNA sequencing (RNA-seq) and multiplexed functional assays, enable the analysis of immune cell function with unprecedented detail and promise not only a deeper understanding of pathogenesis, but also the discovery of novel biomarkers. The large and complex data sets generated by these technologies--big data--require specialized approaches for analysis and visualization of results. Standardization of assays and definition of the range of normal values are additional challenges when translating these novel approaches into clinical practice. In this Review, we discuss technological advances in the high-dimensional analysis of immune cells and consider how these developments might support the discovery of predictive biomarkers to benefit the practice of rheumatology and improve patient care.

Systemic treatment of advanced hepatocellular carcinoma: from disillusions to new horizons

Hepatocellular carcinoma (HCC) is an aggressive malignancy, which accounts for a third of all cancer deaths globally each year. The management of patients with HCC is complex, as both the tumour stage and any underlying liver disease must be considered conjointly. Since the approval of sorafenib in advanced HCC, several phase III clinical trials have failed to demonstrate any superiority over sorafenib in the frontline setting, and no agent has been shown to impact outcomes after sorafenib failure. This review will focus on the range of experimental therapeutics for patients with advanced HCC and highlight the successes and failures of these treatments as well as areas for future development. Specifics such as dose limiting toxicity and safety profile in patients with liver dysfunction related to the underlying chronic liver disease should be considered when developing therapies in HCC. Finally, robust validated and reproducible surrogate end-points as well as predictive biomarkers should be defined in future randomised trials.

From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.