Finding the Right Approach to Big Data-Driven Medicinal Chemistry

Lusher, Scott J.; Ritschel, Tina

doi:10.4155/fmc.15.58

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The current shortcomings of these predictive methods are partly determined by their limited training data, − and this situation is rapidly changing as the amount of available chemical information is exploding. Data-driven drug discovery has great potential to benefit from big-data analysis in medicinal chemistry and chemogenomics. − It is feasible to anticipate that in the very near future the corresponding CADD methodologies will achieve adequate maturity that will cause a paradigm shift in many areas of therapeutics development. The use of mature CADD technology will also significantly expand the repertoire of drug targets, such as bringing in previously unattended functional and protein–protein interact sites, exposed surface areas, and protein–DNA interfaces (as exemplified by our AR DBD project).…”

Section: Summary Of Cadd Best Practicesmentioning

confidence: 99%

Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

Ban

Dalal

et al. 2017

J. Chem. Inf. Model.

104

109

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Small-molecule drug design is a complex and iterative decision-making process relying on pre-existing knowledge and driven by experimental data. Low-molecular-weight chemicals represent an attractive therapeutic option, as they are readily accessible to organic synthesis and can easily be characterized.1 Their potency as well as pharmacokinetic and pharmacodynamic properties can be systematically and rationally investigated and ultimately optimized via expert science behind medicinal chemistry and methods of computer-aided drug design (CADD). In recent years, significant advances in molecular modeling techniques have afforded a variety of tools to effectively identify potential binding pockets on prospective targets, to map key interactions between ligands and their binding sites, to construct and assess energetics of the resulting complexes, to predict ADMET properties of candidate compounds, and to systematically analyze experimental and computational data to derive meaningful structure-activity relationships leading to the creation of a drug candidate. This Perspective describes a real case of a drug discovery campaign accomplished in a relatively short time with limited resources. The study integrated an arsenal of available molecular modeling techniques with an array of experimental tools to successfully develop a novel class of potent and selective androgen receptor inhibitors with a novel mode of action. It resulted in the largest academic licensing deal in Canadian history, totaling $142M. This project exemplifies the importance of team science, an integrative approach to drug discovery, and the use of best practices in CADD. We posit that the lessons learned and best practices for executing an effective CADD project can be applied, with similar success, to many drug discovery projects in both academia and industry.

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Section: Summary Of Cadd Best Practicesmentioning

confidence: 99%

Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action

Ban

Dalal

et al. 2017

J. Chem. Inf. Model.

104

109

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“…Important principle might be hidden behind the big data. For effective analysis, the use of the Internet of things (IoT) together with big data from PAT tool and the models including CFD would bring the rapid decision-making well fused with the practitioner's experiences [81][82][83][84]. e author expects that the operational research based on IoT and big data will be developed to improve the accomplishment of the lyophilization, as shown in Figure 2.…”

Section: Possible Innovation Required To Breakthroughmentioning

confidence: 99%

Recent Development of Optimization of Lyophilization Process

Kawasaki

Shimanouchi

Kimura

2019

Journal of Chemistry

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The objective of this review is to survey the development of the optimization of lyophilization. The optimization study of the lyophilizer has been roughly developing by the order of (i) trial-and-error approach, (ii) process modeling using mathematical models, (iii) scalability, and (iv) quality-by-design. From the conventional lyophilization studies based on the trial-and-error, the key parameters to optimize the operation of lyophilization were found out, i.e., critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs). The mathematical models using the key parameters mentioned above have been constructed from the viewpoints of the heat and mass transfer natures. In many cases, it is revealed that the control of the primary drying stage determines the outcome of the lyophilization of products, as compared with the freezing stage and the secondary drying stage. Thus, the understanding of the lyophilization process has proceeded. For the further improvement of the time and economical cost, the design space is a promising method to give the possible operation range for optimizing the lyophilization operation. This method is to search the optimized condition by reducing the number of key parameters of CMAs, CPPs, and CQAs. Alternatively, the transfer of lyophilization recipe among the lab-, pilot-, and production-scale lyophilizers (scale-up) has been examined. Notably, the scale-up of lyophilization requires the preservation of lyophilization dynamics between the two scales, i.e., the operation of lab- or pilot-scale lyophilizer under HEPA-filtrated airflow condition. The design space determined by focusing on the primary drying stage is large and involves the undesired variations in the quality of final products due to the heterogeneous size distribution of ice crystals. Accordingly, the control of the formation of the ice crystal with large size gave impact on the product quality and the productivity although the large water content in the final product should be improved. Therefore, the lyophilization should take into account the quality by design (QbD). The monitoring method of the quality of the product in lyophilization process is termed the “process analytical technology (PAT).” Recent PAT tools can reveal the lyophilization dynamics to some extent. A combination of PAT tools with a model/scale-up theory is expected to result in the QbD, i.e., a quality/risk management and an in situ optimization of lyophilization operation.

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“…For data-driven medicinal chemistry, integration of internal and external data is a must [ 3 ]. Major public repositories for compounds and activity data from the medicinal chemistry literature and screening campaigns have been established and can be utilized [ 8 , 9 ].…”

Section: Data Integrationmentioning

confidence: 99%

“…In addition, there are other factors that hinder data integration. For example, data quality is a major concern in pharmaceutical research and development [ 3 ] and the quality of external data is often questioned, making medicinal chemists and others reluctant to seriously consider such data. Such concerns are principally valid but must be overcome for data integration, for example, by implementing internal curation protocols.…”

Section: Data Integrationmentioning

confidence: 99%