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The one disease - one target - one drug paradigm was an almost dominant principle in drug discovery from the 1960s to the 2000s. The stagnation and even decline in the productivity of drug innovation around the turn of the millennium and beyond, the realization of limitations of the one-target approach, especially in the treatment of multifactorial diseases, have drawn attention considerably to the one disease - multiple target - one drug multi-targeting drug concept. In this review, we outline old and new molecular design strategies and their practical implementation with own and other examples that also demonstrate unique therapeutic and diagnostic values and benefits of the multi-targeting approach. Finally, we point out that the full potential of the multi-targeting concept can emerge through data analytics and association methods (such as artificial intelligence) and system-based approach, preferably by linking it to quantitative systems pharmacology. This new systems pharmacology drug approach may also lead to novel breakthrough drugs, drug combinations and drug repositioning. Orv Hetil. 2020; 161(14): 523-531.

Introduction: Data during routine patient care are created in multiple digital and paper-based hardcopy systems, therefore their retrieval is cumbersome in the follow-up of patients. Multiple sclerosis is the most prevalent neurological disorder in the young age, with major consequences on health and socio-economic status., Aim: We set forth to create a user-friendly, detailed local database where it is easy to access, register and analyze data. Based on our experiences during building this registry, we develop the model of a modern type of database., Method: First we established a local registry in Excel, then data were transferred to the worldwide used iMed system. Separate pages were used to register basic data, follow-up visits, relapses, accompanying diseases, results of neuroimaging, cerebrospinal fluid, evoked response and other tests, pharmacological and non-pharmacological treatments., Results: The database currently contains data of 316 patients. MRI was performed in 96%, cerebrospinal fluid examination in 45% of the patients. The rate of primary progressive disease at disease onset is 9%. Disease modifying treatments were applied in 82% of the patients., Conclusion: The traditional manual data entry and data export in PDF format is obsolete and time-consuming. The development of local disease-specific databases appropriate for clinical and research purposes requires continuous and mostly automatic data entry. In future local registries the establishment of uniform documentational language and structure, and automatic transfer of information among different digital systems are required. We present the model of such a registry, which is based on a healthcare data lake. Orv Hetil. 2019; 160(4): 131-137.

Vast amounts of data are created during routine patient care which are stored in unstructured digital and hardcopy formats in healthcare institutions. Analysis of large databases help to define the healthcare needs of the population and to organize healthcare services for specific diseases. As a model, we selected multiple sclerosis (MS), a disease with well-defined diagnostic criteria, a usually inpatient initial diagnosis, and a need for regular outpatient check-up. Using multiple sclerosis as an example, we set forth to screen and analyze international and Hungarian databases. In the framework of the initiation of the data lake system of Semmelweis University, we aim to define features of the data system needed for disease-specific databases for future applications. To determine essential data-entry criteria for such a database, we review the most important multiple sclerosis registries. We evaluate the type of registered data, structure of database, privacy issues, the availability and ways of application of the databases. Initially, the MS databases were created locally, aiming for better care of patients. As a further step, data were collected for scientific research by national and international co-operations. Disease-specific databases have become of high priority for national healthcare providers, and long-term information on a population ("real-world" data) is extremely important to assess the effectivity and safety of a treatment at the population level. Our analysis contributes to a project which focuses on the aspects of developing a data lake at a service provider level including clinical, diagnostic and digital healthcare departments of Semmelweis University, Budapest, Hungary. Orv Hetil. 2019; 160(4): 123-130.

The rapidly accumulating, heterogeneous, numerous biomedical data and the increasing knowledge represent both the key areas and the major bottleneck in the complete realization of personalized medicine. Traditional clinical aims as well as decision-making processes in drug research need systems-based integration of data and their analyses, which require a wide range of new mathematical approaches and application of information technologies. In the world of biomarkers, all such tasks correspond to three types of biomarkers, namely, endpoint, target and diagnostic biomarkers, which together form a complex network. To decipher the networks, probabilistic graphical models are introduced. Briefly, the authors illustrate their use for exploration and visualization of direct dependencies among biomarkers. Finally, construction of decision networks and their application to structural and quantitative optimization of biomarkers are discussed to provide a novel type of supporting tools for clinical practice.