A Perspective on Innovating the Chemistry Lab Bench.

Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.

Translational in vitro research: integrating 3D drug discovery and development processes into the drug development pipeline.

As we witness steady progress towards the development of robust, scalable, and reproducible 3D tissue models for preclinical drug testing, there is a need for systematic physiological and pharmacological validation and benchmarking. Ongoing and future studies should generate evidence as to whether 3D tissue models are more predictive, help reduce the risk of failure rate, and can be used for decision making in the drug discovery and development pipeline. Here, we discuss the importance of harmonizing the validation of these models based on throughput capacity and physiological complexity as a requirement to establish their true translational capacity. We also outline our strategy for a novel 3D-tailored holistic drug discovery concept rather than piecemeal integration of 3D models into the current process.

Twenty-First Century Diseases: Commonly Rare and Rarely Common?

Alzheimer's drugs are failing at a rate of 99.6%, and success rate for drugs designed to help patients with this form of dementia is 47 times less than for drugs designed to help patients with cancers ( www.scientificamerican.com/article/why-alzheimer-s-drugs-keep-failing/2014 ). How can it be so difficult to produce a valuable drug for Alzheimer's disease? Each human has a unique genetic and epigenetic makeup, thus endowing individuals with a highly unique complement of genes, polymorphisms, mutations, RNAs, proteins, lipids, and complex sugars, resulting in distinct genome, proteome, metabolome, and also microbiome identity. This editorial is taking into account the uniqueness of each individual and surrounding environment, and stresses the point that a more accurate definition of a "common" disorder could be simply the amalgamation of a myriad of "rare" diseases. These rare diseases are being grouped together because they share a rather constant complement of common features and, indeed, generally respond to empirically developed treatments, leading to a positive outcome consistently. We make the case that it is highly unlikely that such treatments, despite their statistical success measured with large cohorts using standardized clinical research, will be effective on all patients until we increase the depth and fidelity of our understanding of the individual "rare" diseases that are grouped together in the "buckets" of common illnesses. Antioxid. Redox Signal. 27, 511-516.

A multicenter pilot study examining the role of circulating tumor cells as a blood-based tumor marker in patients with extensive small-cell lung cancer.

BACKGROUND: Small-cell lung cancer (SCLC), a variant of lung cancer marked by early metastases, accounts for 13% of all lung cancers diagnosed in US. Despite high response rates to treatment, it is an aggressive disease with a median survival of 9-11 months for patients with extensive stage (EX-SCLC). Detection of circulating tumor cells (CTCs) is a novel laboratory technique currently in use to determine response to therapy and to predict prognosis in breast, colorectal, and prostate cancer. We initiated a pilot study to analyze the role of CTCs as a biomarker of response and relapse in patients with EX-SCLC. METHODS: We collected blood samples from chemotherapy naïve patients with EX-SCLC prior to initiation of therapy, after completion of systemic therapy, and follow-up every 6-8 weeks and at relapse. The number of CTCs was determined using the cell search system in a central laboratory. The study was conducted in four different sites, and it was reviewed and approved by respective research review committees and IRBs. RESULTS: We enrolled 26 patients with EX-SCLC, 1 was excluded due to ineligibility, all were treated with platinum and etoposide. We observed partial response in 16 patients, stable disease in 3 patients, 1 patient with disease progression, and 6 patients were not assessed (5 deceased, 1 not available). The overall median number of CTCs in 24 patients measured at baseline and post-tx was 75 (range 0-3430) and 2 (range 0-526), respectively. A significant reduction in CTCs from baseline to post-treatment was identified for 15 subjects; the median reduction was 97.4% (range -100 to +100%, p < 0.001). Higher baseline CTCs and percentage change in post-treatment CTCs were associated with decreased survival. CONCLUSION: We demonstrated that it is feasible to detect CTCs in EX-SCLC. If validated in other prospective studies, CTCs could be a useful biomarker in the management of EX-SCLC by predicting patients' clinical responses to therapy.

Activity of anticancer agents in a three-dimensional cell culture model.

Cell-monolayer-based assays for chemotherapeutic drug discovery have proven to be highly artificial compared with physiological systems. The objective of this study was to culture cancer cells in a simple 3-dimensional (3D) collagen gel model to study the antiproliferative activity of known lung cancer drugs. The validity of our 3D model was tested by measuring the activity of 10 lung cancer drugs (Paclitaxel, Alimta, Zactima, Doxorubicin, Vinorelbine, Gemcitabine, 17AAg, Cisplatin, and 2 experimental drugs from the University of Kansas [KU174 and KU363]) in 2 lung cancer cell lines (A549 and H358) and comparing the activity in a traditional 2-dimensional (2D) in vitro cellular assay. Both potency and efficacy of these drugs were calculated to evaluate the activity of the drugs. Our results demonstrate that the activity of these drugs showed significant differences when tested in 3D cultures, which varied with individual drugs and the cell line used for testing. For example, the cytotoxicity of Paclitaxel, KU174, Alimta, Zacitma, Doxorubicin, Vinorelbine, KU363, and 17AAg was significantly changed when tested in the 3D model, whereas the potency of Cisplatin and Gemcitabine in H358 cell line remained unaffected. A similar pattern, with some differences, was observed in A549 cells and is discussed in detail in this article. The observed differences in potency and efficacy of the cancer drugs in 3D models suggest that the biological implications of screening configurations should be taken into account to select superior cancer drug candidates in preclinical studies.