Masahiko Nakatsui

M-path: A Compass for Navigating Potential Metabolic Pathways

MOTIVATION Construction of synthetic metabolic pathways promises sustainable production of diverse chemicals and materials. To design synthetic metabolic pathways of high value, computational methods are needed to expand present knowledge by mining comprehensive chemical and enzymatic information databases. Several computational methods have been already reported for the metabolic pathway design, but until now computation complexity has limited the diversity of chemical and enzymatic data used. RESULTS We introduce a computational platform, M-path, to explore synthetic metabolic pathways including putative enzymatic reactions and compounds. M-path is an iterative random algorithm, which makes efficient use of chemical and enzymatic databases to find potential synthetic metabolic pathways. M-path can readily control the search space and perform well compared with exhaustively enumerating possible pathways. A web-based pathway viewer is also developed to check extensive metabolic pathways with evaluation scores on the basis of chemical similarities. We further produce extensive synthetic metabolic pathways for a comprehensive set of alpha amino acids. The scalable nature of M-path enables us to calculate potential metabolic pathways for any given chemicals.

Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants.

Chemical genomics research has revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale. We also address the question of how to select plausible negative noninteraction pairs based on a statistical approach in order to develop reliable prediction models for GPCR-ligand interactions. The key interaction sites estimated by our approach can be of great use not only for screening of active compounds but also for modification of active compounds with the aim of improving activity or selectivity.

Constructing a Foundational Platform Driven by Japan’s K Supercomputer for Next-Generation Drug Design

The cost of pharmaceutical R&D has risen enormously, both worldwide and in Japan. However, Japan faces a particularly difficult situation in that its population is aging rapidly, and the cost of pharmaceutical R&D affects not only the industry but the entire medical system as well. To attempt to reduce costs, the newly launched K supercomputer is available for big data drug discovery and structural simulation‐based drug discovery. We have implemented both primary (direct) and secondary (infrastructure, data processing) methods for the two types of drug discovery, custom tailored to maximally use the 88 128 compute nodes/CPUs of K, and evaluated the implementations. We present two types of results. In the first, we executed the virtual screening of nearly 19 billion compound‐protein interactions, and calculated the accuracy of predictions against publicly available experimental data. In the second investigation, we implemented a very computationally intensive binding free energy algorithm, and found that comparison of our binding free energies was considerably accurate when validated against another type of publicly available experimental data. The common feature of both result types is the scale at which computations were executed. The frameworks presented in this article provide prospectives and applications that, while tuned to the computing resources available in Japan, are equally applicable to any equivalent large‐scale infrastructure provided elsewhere.

Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer

Objectives We aimed to examine the association between homologous recombination repair (HRR)-related gene mutations and efficacy of oxaliplatin-based chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). Results Non-synonymous mutations in HRR-related genes were found in 13 patients and only one patient had a family history of pancreatic cancer. Eight patients with HRR-related gene mutations (group A) and nine without HRR-related gene mutations (group B) received oxaliplatin-based chemotherapy. Median progression-free survival after initiation of oxaliplatin-based chemotherapy was significantly longer in group A than in group B (20.8 months vs 1.7 months, p = 0.049). Interestingly, two patients with inactivating HRR-related gene mutations who received FOLFIRINOX as first-line treatment showed exceptional responses with respect to progression-free survival for > 24 months. Materials and Methods Complete coding exons of 12 HRR-related genes (ATM, ATR, BAP1, BRCA1, BRCA2, BLM, CHEK1, CHEK2, FANCA, MRE11A, PALB2, and RAD51) were sequenced using a Clinical Laboratory Improvement Amendment-certified multiplex next-generation sequencing assay. Thirty consecutive PDAC patients who underwent this assay between April 2015 and July 2017 were included. Conclusions Our results suggest that inactivating HRR-related gene mutations are predictive of response to oxaliplatin-based chemotherapy in patients with PDAC.

Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors

Advances in next‐generation sequencing (NGS) technologies have enabled physicians to test for genomic alterations in multiple cancer‐related genes at once in daily clinical practice. In April 2015, we introduced clinical sequencing using an NGS‐based multiplex gene assay (OncoPrime) certified by the Clinical Laboratory Improvement Amendment. This assay covers the entire coding regions of 215 genes and the rearrangement of 17 frequently rearranged genes with clinical relevance in human cancers. The principal indications for the assay were cancers of unknown primary site, rare tumors, and any solid tumors that were refractory to standard chemotherapy. A total of 85 patients underwent testing with multiplex gene assay between April 2015 and July 2016. The most common solid tumor types tested were pancreatic (n = 19; 22.4%), followed by biliary tract (n = 14; 16.5%), and tumors of unknown primary site (n = 13; 15.3%). Samples from 80 patients (94.1%) were successfully sequenced. The median turnaround time was 40 days (range, 18–70 days). Potentially actionable mutations were identified in 69 of 80 patients (86.3%) and were most commonly found in TP53 (46.3%), KRAS (23.8%), APC (18.8%), STK11 (7.5%), and ATR (7.5%). Nine patients (13.0%) received a subsequent therapy based on the NGS assay results. Implementation of clinical sequencing using an NGS‐based multiplex gene assay was feasible in the clinical setting and identified potentially actionable mutations in more than 80% of patients. Current challenges are to incorporate this genomic information into better therapeutic decision making.

The Effect of Conformational Flexibility on Binding Free Energy Estimation between Kinases and Their Inhibitors

Accurate prediction of binding affinities of drug candidates to their targets remains challenging because of protein flexibility in solution. Conformational flexibility of the ATP-binding site in the CDK2 and ERK2 kinases was identified using molecular dynamics simulations. The binding free energy (ΔG) of twenty-four ATP-competitive inhibitors toward these kinases was assessed using an alchemical free energy perturbation method, MP-CAFEE. However, large calculation errors of 2-3 kcal/mol were observed using this method, where the free energy simulation starts from a single equilibrated conformation. Here, we developed a new ΔG computation method, where the starting structure was set to multiconformations to cover flexibility. The calculation accuracy was successfully improved, especially for larger molecular size compounds, leading to reliable prediction of a broader range of drug candidates. The present study demonstrates that conformational flexibility of interactions between a compound and the glycine-rich loop in the kinases is a key factor in ΔG estimation.

Chemogenomic approach to comprehensive predictions of ligand-target interactions: A comparative study

Chemogenomics has emerged as an interdisciplinary field that aims to ultimately identify all possible ligands of all target families in a systematic manner. An ever-increasing need to explore the vast space of both ligands and targets has recently triggered the development of novel computational techniques for chemogenomics, which have the potential to play a crucial role in drug discovery. Among others, a kernel-based machine learning approach has attracted increasing attention. Here, we explore the applicability of several ligand-target kernels by extensively evaluating the prediction performance of ligand-target interactions on five target families, and reveal how different combinations of ligand kernels and protein kernels affect the performance and also how the performance varies between the target families.

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

Background We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data. Methods Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings. Results A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models. Conclusion By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.

A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection

Recent innovations in next-generation sequencing (NGS) technologies have enabled comprehensive genomic profiling of human cancers in the clinical setting. The ability to profile has launched a worldwide trend known as precision medicine, and the fusion of genomic profiling and pharmacogenomics is paving the way for precision medicine for cancer. The profiling is coupled with information about chemical therapies available to patients with specific genotypes. As a result, the chemogenomic space in play is not only the standard chemical and genome space but also the mutational genome and chemical space. In this chapter, we introduce clinical genomic profiling using an NGS-based multiplex gene assay (OncoPrime™) at Kyoto University Hospital.

Abstract 4496: Genomic profiling of late stage tumors powering precision medicine in Japan

Much evidence has shown that genomic profiling of tumors provides patients with treatment options specific to their tumor9s biology in the US. Within the Kyoto University Hospital Cancer Center, we offer patients access to these tests through the OncoPrimeTM for those patients of cancer with unknown primary (CUP), rare tumor types or for which standard treatment options have failed. Our institution sees over 6,000 cancer patients each year with over 1,500 receiving chemotherapy and a progressive climate makes this kind of testing favorable. We aim to demonstrate the efficacy of genomic profiling along with successful execution of international specimen logistics as FFPE samples are shipped to EA Genomics and clinical report is provided within 3 weeks. We officially launched the product publicly in March 2015 and the majority of patients have paid for testing out of pocket in lieu of insurance coverage. During this first year, we saw 30% of patients with CUPs, and 16% of both pancreas and biliary tract with the rest being other rare cancers. Patients going through the testing were generally successful (93.5%) with the specimen failures likely indicative with FFPE artifacts or severe tumor heterogeneity. Actionabilty for our population (84.8%) is consistent with previously published numbers for information provided in the clinical report. Additional information was collected after the clinical reports to determine early indicators of follow up and see that the accepted term ‘actionability’ holds different meaning during the practice of patient care. While we had 17% of patients receive the suggested indication we have yet seen positive response from patients who have returned for follow-up. Selected tissues were tested by alternative profiling vendors, and in one case alternate action with Erlotinib was recommended by one vendor; this action has shown promising response. This suggests that heterogeneous interpretation is still a largely unsolved issue with genomic profiling. This may suggested that interpretation is still a largely unsolved issue with genomic profiling. While almost all other patients tested qualified for clinical trials (84.8%) we noted 3 main barriers to entry: geography, cost of intervention and deterioration of patient condition. We show our results in line with previous studies but strive to increase adoption of results and are compiling information of pan cancer biomarker statuses and treatment success/failures to improve interpretation prior to wider adoption of this kind of testing. Citation Format: Masashi Kanai, Matthew Schu, Tomohiro Sakuma, Steven Abbott, Tadayuki Kou, Ross Gagnon, Thomas Halsey, Shigemi Matsumoto, Mayumi Kamada, Masahiko Nakatsui, Yasushi Okuno, Akinori Hiroshima, Hiroaki Mochizuki, Patrick Hurban, Manabu Muto, Victor J. Weigman. Genomic profiling of late stage tumors powering precision medicine in Japan. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4496.