The Importance of Biological Invariance in Drug Design

Introduction

Drug discovery comprises the screening of new compounds to rise some desired properties, while reducing the result of potential side effects and improving their efficacy towards a certain disease. All these assets should surge the chance of success in the clinical trials, since the drug development is a capital-intensive procedure. The average cost of developing a new medicine has been appraised to be 2.8 billion dollars [1]. The main reason behind this expenditure is a poor understanding of the disease expansion mechanisms, and consequently; being able to design and reposition drugs that optimally target the actionable genes, while minimizing side effects. Both are opened problems in pharmaco-genomics, which represent the forefront research in the pharmaceutical industry. To allow Precision Medicine, genomic kits are needed to easier diagnosis and adopt optimum decisions [2]. In this sense, the concept of biological invariance seems a promising asset to understand genomics, regardless the methodology employed, since the analysis of the altered pathways is independent of the sampling methodology utilized [3].

Genomics and the Phenotype Prediction Problem

Predicting and characterizing phenotypes from genomics is a complex and undetermined problem, due to the fact the number of monitored probes exceeds the number of samples. To solve this kind on problems the design of mathematical models, called classifiers, that link the genetic signatures to the classes in which the phenotype is divided. The associated uncertainty space to the classifier L* (g), Mtol={g: O(g)<Etol }, is formed by the sets of high predictive networks with similar predictive accuracy, in other words, the sets of genes g whose prediction error, O(g) , is lower than a certain Etol. These sets are in flat curvilinear valleys, making the sampling and identification complex and arduous [4-6]. A wide set of algorithms could be utilized to tackle the prediction and classification of altered pathways involved in disease development, such as Nearest-Neighbor classifiers [7-8] Random Forest [9], Extreme Learning Machines [10], Support Vector Machines [11] etc. These algorithms, provided the fact that the noise and the data has been properly treated to minimize its impact in the posterior analysis of the results, would lead to the same results, as reported by Cernea et al. [3]. Consequently, biological invariance, a concept that implies that the high discriminatory genetic networks are located within the neighborhood of Mtol and the altered pathways are independent of the classifier and the sampling method utilized to unravel them, would help in the drug design and repositioning process.

Drug Design and Repositioning

After a robust sampling of the genetic pathways, an optimum selection of potential drugs can be carried out. This methodology will dramatically increase the approval of drugs and reposition the existing ones. In the present days, there are a wide range of models utilized in drug design, from models based on large data sets of chemicals, compositions and disease-drug activity, known as perturbation models [12], to multiscale models that are capable of integrating different genomic, proteomic and metabolomic information [13]. Furthermore, a new paradigm known as de novo multiscale modelling has emerged, which allows the design of a new drug within the chemical subspace where it could be beneficious [14]. However, regardless of the methodology utilized, powerful AI tools are required in order to integrate information from different types of drugs to biological data and disease specific network studies that serve to identify new potential target [15]. In addition, AI shall be utilized to determine from complex genetic data and all the research information available the interaction a drug may have with its primary target (that tackle the disease) and secondary targets (that causes side-effects). This process is supposed to be, such as in the sampling of altered pathways, independent from the methodology.

Conclusion

Further research and development of AI methods are required to confirm the hypothesis of Biological Invariance, however, it offers a tantalizing approach to handle this complex and difficult problem in the pharmaceutical industry. The mentioned concept would enable the design of affordable genetic tools that would enhance the precision medicine. However, further confirmation is required, being the absence of data one of the major drawback. Biological invariance suggests that any AI protocol to enhance drug design should be iterative and learn from experience. Algorithms and methodologies shall be kept simple and fast, since they would yield to the same results and with the same accuracy.