Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test.

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.

Risk Factors Causing Hypothyroidism in Patients With Head and Neck Cancer After Radiotherapy Using SIB-VMAT.

BACKGROUND/AIM: We evaluated the incidence of radiation-induced hypothyroidism and its risk factors in patients with head and neck cancer who underwent radiotherapy using simultaneous integrated boost-volumetric-modulated arc therapy (SIB-VMAT). PATIENTS AND METHODS: This retrospective study included 86 patients who received definitive radiotherapy using SIB-VMAT for head and neck cancer. The incidence of ≥ grade 2 hypothyroidism was evaluated. We also evaluated the relationships between hypothyroidism development and clinical factors and thyroid dose-volume parameters. RESULTS: During a median follow-up period of 17 months (range=3-65 months), 31 patients (36.0%, 31/86) developed grade 2 hypothyroidism requiring hormone replacement therapy. No patients experienced ≥ grade 3 hypothyroidism. The cumulative incidences of hypothyroidism at 1 and 2 years after radiation therapy were 24.5% and 38.7%, respectively, with a median onset time of 10.0 months (range=3.0-35.0 months). Thyroid volume (p=0.003), volume of the thyroid spared at 60 Gy (VS60; cut-off value, 5.16 ml; p=0.009), VS70 (cut-off value, 8.0 ml; p=0.007), VS60 equivalent dose in 2 Gy fraction (EQD2; cut-off value, 7.78 ml; p=0.001), and VS70EQD2 (cut-off value, 10.59 ml; p=0.008) were significantly associated with the development of radiation-induced hypothyroidism. CONCLUSION: Radiation-induced hypothyroidism is not rare in patients with head and neck cancer undergoing radiotherapy using SIB-VMAT. Radiation dose-volume parameters detected in this study may be useful indicators to prevent this complication.

Clinical Experience of Percutaneous Radiofrequency Ablation Using an arfa RF ABLATION SYSTEMⓇ in Various Organs.

Purpose: To evaluate the feasibility, safety, and efficacy of radiofrequency (RF) ablation using an ablation system (arfa RF ABLATION SYSTEMⓇ; Japan Lifeline Co. Ltd.) for treating solid tumors in various organs. Material and Methods: Between October 2019 and August 2021, 80 patients (29 women, 51 men; median age, 70.0 yr) underwent 107 RF ablation sessions using the ablation system to treat 151 tumors in the liver (n = 86), lung (n = 51), adrenal gland (n = 4), pleura (n = 4), bone (n = 3), lymph node (n = 2), and kidney (n = 1). The maximum tumor diameter was 2-40 mm (median, 11 mm). This study evaluated technical success (defined as the completion of planned RF ablation), technique efficacy (defined as the complete tumor ablation on follow-up images), and adverse events. Local tumor progression in 146 curatively treated malignant tumors was evaluated. Results: The technical success rate was 100% (107/107). Ablation zones in two tumors were insufficient. Therefore, the primary technique efficacy rate was 98.1% (105/107). Grade 3 hepatic infarction (1.6%, 1/64) and grade 4 pleuritis (3.4%, 1/29) occurred respectively after liver and lung RF ablation. During the median follow-up period of 10.2 months (Interquartile range, 4.2 and 16.4 months), local tumor progression developed in two tumors (1.4%, 2/146). Conclusions: The arfa RF ABLATION SYSTEMⓇ is a feasible, safe, and effective RF ablation device for managing solid tumors in various organs.

Protective Effect of Polaprezinc and Hyperbaric Oxygen Therapy on Radiation-induced Small Intestinal Damage in Mice.

BACKGROUND/AIM: To investigate the effect of polaprezinc (antioxidant) administration and hyperbaric oxygen therapy on radiation-induced intestinal injury. MATERIALS AND METHODS: Forty-five C57BL/6J mice underwent total body radiation of 2 Gy. Polaprezinc was given in 12 mice, hyperbaric oxygen in 12 mice, and both in 12 mice. The other 9 mice did not undergo any treatment. Mice were sacrificed 2, 4, and 6 h after radiation, and 9 specimens (3 each from the duodenum, jejunum, and ileum) were harvested. Apoptotic intestinal crypt cells were histologically evaluated by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. RESULTS: Apoptotic cell number per 1,000 crypt cells was 31.0±6.7 at 2 h, 28.4±5.2 at 4 h, and 32.9±5.1 at 6 h in the mice group treated by radiation alone. Both polaprezinc administration and hyperbaric oxygen therapy significantly suppressed apoptosis. Although the effect of polaprezinc administration on suppressing apoptosis became less over time (4.9±5.7 and 19.4±13.2 at 2 and 6 h, respectively), that of hyperbaric oxygen therapy was stable regardless of time (23.6±4.8 and 25.8±4.1 at 2 and 6 h). Administration of both polaprezinc and hyperbaric oxygen showed a significant synergetic or additive effect on suppressing apoptosis at 6 h (11.4±10.5, p<0.0035 vs. polaprezinc, p<0.0001 vs. hyperbaric oxygen). CONCLUSION: Both polaprezinc administration and hyperbaric oxygen therapy are effective in relieving radiation-induced small intestinal damage, and a synergistic or additive effect is expected when using both.

Use of Microballoon Catheter in Transarterial Ethanol Embolization of Renal Angiomyolipoma: A Retrospective Comparative Study with Historical Control?

Purpose: To clarify the utility of microballoon catheter in renal arterial ethanol embolization of renal angiomyolipoma (AML). Material and Methods: A total of 20 patients (15 women, 5 men) with median age of 45 years (39-60 years) underwent embolization to treat 22 AMLs. A mixture of ethanol and iodized oil was injected into the feeding arteries of 13 tumors using balloon occlusion (the balloon embolization group) with a microballoon catheter and 9 tumors without using balloon occlusion (the non-balloon embolization group). Changes in the maximum tumor diameter, tumor volume, and adverse events were evaluated. Result: The median baseline maximum tumor diameters and volumes were 6.3 cm and 61.4 cm3 in the balloon embolization group, and 4.6 cm and 40.1 cm3 in the non-balloon embolization group, respectively. Tumor enhancement disappeared on postembolization angiography in all cases. All tumors shrunk after embolization. There were no statistically significant differences in the percent decrease in the maximum tumor diameter and volume at 10-12 month between balloon occlusion group (31.5% and 67.9%) and control group (34.8% and 62.6%). Fever was significantly more frequent when balloon occlusion was used: 38% vs. 0% (p = 0.03). No major complication was observed in either patient group. Conclusions: Balloon occlusion may not affect tumor shrinkage when embolizing AMLs with a mixture of ethanol and lipiodol.

Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data.

In drug discovery, the prediction of activity and absorption, distribution, metabolism, excretion, and toxicity parameters is one of the most important approaches in determining which compound to synthesize next. In recent years, prediction methods based on deep learning as well as non-deep learning approaches have been established, and a number of applications to drug discovery have been reported by various companies and organizations. In this research, we performed activity prediction using deep learning and non-deep learning methods on in-house assay data for several hundred kinases and compared and discussed the prediction results. We found that the prediction accuracy of the single-task graph neural network (GNN) model was generally lower than that of the non-deep learning model (LightGBM), but the multitask GNN model, which combined data from other kinases, comprehensively outperformed LightGBM. In addition, the extrapolative validity of the multitask model was verified by using it for prediction on known kinase ligands. We observed an overlap between characteristic protein-ligand interaction sites and the atoms that are important for prediction. By building appropriate models based on the conditions of the data set and analyzing the feature importance of the prediction results, a ligand-based prediction method may be used not only for activity prediction but also for drug design.

From traditional to data-driven medicinal chemistry: A case study.

Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and efforts until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data science into practical medicinal chemistry and quantify the impact, as reported herein. Although characteristic features and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists.

Searchable database of frequent R-groups in medicinal chemistry and their preferred replacements.

In compound optimization, analogue series (ASs) are generated by introducing different R-groups (substituents, functional groups) at specific substitution sites. Systematic investigations of R-groups in medicinal chemistry have so far been rare. We have carried out a large-scale computational analysis of R-groups on the basis of ASs covering currently available bioactive compounds (Takeuchi et al., 2021). With the aid of a network data structure, frequently used R-groups and preferred replacements were identified. On the basis of these data, R-group replacement hierarchies were derived and organized in a searchable database that is made freely available. This contribution complements our systematic analysis (Takeuchi et al., 2021) by specifying the data we have generated and detailing their open access deposition.

Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry.

Selection of R-groups (substituents, functional groups) is of critical importance for the generation of analogues during hit-to-lead and lead optimization. In the practice of medicinal chemistry, R-group selection is mostly driven by chemical experience and intuition taking synthetic criteria into account. However, systematic analyses of substituents are currently rare. In this work, we have computationally isolated R-groups from more than 17,000 analog series comprising ∼315,000 bioactive compounds. From more than 50,000 unique substituents, frequently used R-groups were identified. For these R-groups, preferred replacements over more than 60,000 individual substitution sites were identified with the aid of a network data structure. These data provided the basis for the generation of a searchable R-group replacement system for medicinal chemistry containing replacement hierarchies for frequently used R-groups, which is made freely available as the central component of our study.

R-group replacement database for medicinal chemistry.

AIM: Generation of an R-group replacement system for compound optimization in medicinal chemistry. MATERIALS & METHODS: From bioactive compounds, analogue series (ASs) were systematically extracted and from these ASs, all R-groups were isolated and further analyzed. EXEMPLARY RESULTS & DATA: From more than 17,000 ASs, more than 50,000 unique R-groups were isolated. For the 500 most frequently used R-groups, preferred replacements were identified and organized in hierarchies. All original data and an R-group replacement database are made available in an open access deposition. LIMITATIONS & NEXT STEPS: The searchable database has no limitations and can easily be modified using the source data we provide. The next step will be applying this R-group resource in practical medicinal chemistry projects as decision support.

CT Findings of Primary Renal Angiosarcoma.

Primary angiosarcomas of the kidney are very rare but highly aggressive tumors showing poor prognosis. We present a case of primary renal angiosarcoma occurring in a 60-year-old man with left flank pain. CT images depicted a huge exophytic mass (14 cm in diameter) in the left kidney, exhibiting central extensive hemorrhage or necrosis without contrast enhancement. The mass showed centripetal peripheral nodular enhancement on dynamic contrast-enhanced CT images. We suggest its inclusion in the differential diagnosis of cases of hemorrhagic renal tumors with prominent vasculature.

Metastasis-Directed Radiotherapy for Oligoprogressive Castration-Resistant Prostate Cancer Recurrence Revealed by Choline PET/CT.

We report a 49-year-old male with castration-resistant prostate cancer (CRPC) with oligometastasis diagnosed by 11C-choline positron emission tomography-computed tomography (PET/CT) and treated with target radiotherapy. In the diagnosis of CRPC (serum prostate-specific antigen [PSA] level of 6.53 ng/mL after maximum androgen blockade (MAB) therapy, high-dose brachytherapy, and external beam radiotherapy), 11C-choline PET/CT detected one tiny obturator lymph node metastasis which fluorodeoxyglucose PET/CT could not detect. He underwent intensity-modulated radiation therapy and MAB was restarted. The PSA value decreased and reached nadir (0.091 ng/mL) after 6 months. The time to PSA progression was 10 months. The choline PET/CT finding and the corresponding local treatment could play an important role in the management sequence of oligoprogressive CRPC.

Global Assessment of Substituents on the Basis of Analogue Series.

While bioisosteric replacements have been extensively investigated, comprehensive analyses of R-/functional groups have thus far been rare in medicinal chemistry. We introduce a new analysis concept for the exploration of chemical substituent space that is based upon bioactive analogue series as a source. From ∼24,000 analogue series, more than 19,000 substituents were isolated that were differently distributed. A subset of ∼400 substituent fragments occurred most frequently in different structural contexts. These substituents contained well-known R-groups as well as novel structures. Substitution site-specific replacement and network analysis revealed that chemically similar substituents preferentially occurred at given sites and identified intuitive substitution pathways that can be explored for compound design. Taken together, the results of our analysis provide new insights into substituent space and identify preferred substituents on the basis of analogue series. As a part of our study, all the data reported are made freely available.

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions.

Assessing the degree to which analogue series are chemically saturated is of major relevance in compound optimization. Decisions to continue or discontinue series are typically made on the basis of subjective judgment. Currently, only very few methods are available to aid in decision making. We further investigate and extend a computational concept to quantitatively assess the progression and chemical saturation of a series. To these ends, existing analogues and virtual candidates are compared in chemical space and compound neighborhoods are systematically analyzed. A large number of analogue series from different sources are studied, and alternative chemical space representations and virtual analogues of different designs are explored. Furthermore, evolving analogue series are distinguished computationally according to different saturation levels. Taken together, our findings provide a basis for practical applications of computational saturation analysis in compound optimization.

Combining Similarity Searching and Network Analysis for the Identification of Active Compounds.

A variety of computational screening methods generate similarity-based compound rankings for hit identification. However, these rankings are difficult to interpret. It is essentially impossible to determine where novel active compounds might be found in database rankings. Thus, compound selection largely depends on intuition and guesswork. Herein, we show that molecular networks can substantially aid in the analysis of similarity-based compound rankings. A series of networks generated for rankings provides visual access to search results and adds chemical neighborhood and context information for reference compounds that are not available in rankings. Network structure is shown to serve as a diagnostic criterion for the likelihood to successfully select active compounds from rankings. In addition, comparison of different networks makes it possible to prioritize alternative similarity measures for search calculations and optimize the enrichment of active compounds in rankings.

Design of a tripartite network for the prediction of drug targets.

Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

Computational method for estimating progression saturation of analog series.

In lead optimization, it is difficult to estimate when an analog series might be saturated and synthesis of additional compounds would be unlikely to yield further progress. Rather than terminating a series, one often continues to generate analogs hoping to reach the final optimization goal, even if obstacles that are faced ultimately prove to be unsurmountable. Clearly, methodologies to better understand series progression and saturation are highly desirable. However, only a few approaches are currently available to monitor series progression and aid in decision making. Herein, we introduce a new computational method to assess progression saturation of an analog series by relating the properties of existing compounds to those of synthetic candidates and comparing their distributions in chemical space. The neighborhoods of analogs are analyzed and the distance relationships between existing and candidate compounds quantified. An intuitive dual scoring scheme makes it possible to characterize analog series and their degree of progression saturation.

From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets.

The analysis of structure-activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.

Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks.

Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds.

AIM: Computational exploration of small-molecule-based relationships between target proteins from different families. MATERIALS & METHODS: Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data. RESULTS: A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets. CONCLUSION: Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available.