Challenges in natural product-based drug discovery assisted with in silico-based methods

Abstract

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

Fig. 1. Examples of well-known NPs.

Fig. 2. Ro5 evaluation of StreptomeDB 2.0. (A) MW, (B) HBD, (C) log P, (D) HBA, (E) NRB. The original figure was published under a Creative Commons License.

Fig. 3. Pairwise comparison of Ro5 descriptors of StreptomeDB 2.0. (A) MW against log P, (B) MW against HBA, (C) MW against HBD and (D) MW against NRB. The original figure was published under a Creative Commons License.

Fig. 4. PCA of computed descriptors. The original figure was published under a Creative Commons License.

Fig. 5. PCA on ADMET descriptors for selected compound datasets (variance = 56.2%). The original figure was published under a Creative Commons License.

Fig. 6. The network displayed common repurposed drugs between different viruses using the pipeline generated by Rajput et al. (A) Correlations between the repurposed drugs identified using the “drug-target-drug” approach and 14 viruses causing epidemics/pandemic. (B) Interaction diagram of identified repurposed drugs found in common for more than five viruses (figure reproduced with permission).

Fig. 7. Virtual hits from ANPDB as HDAC inhibitors.

Fig. 8. 13 out of the 22 suggested virtual hits: black (>1 mg), red (tested negative) and green (active).

Conrad V. Simoben

Smith B. Babiaka

Aurélien F. A. Moumbock

José L. Medina-Franco

Fidele Ntie-Kang

Wolfgang Sippl