370

27

Drug Discovery

value obtained from this kind of study are dynamical ones, such as rate of spread-

ing. Because this approach is still rather new, results are sparse; as they accumulate

it should become possible to correlate characteristic morphological dynamics with

beneficial or harmful effects on the cell.

Traditionally animal models were used to test candidate molecules pre-clinically.

But this is problematical, because despite many shared systems no animal exactly

resembles a human being. Hence results may be seriously misleading and have had

tragic consequences. Furthermore, there is a growing general aversion in society to

the use of animals for testing drugs and other products.

Modeling provides another alternative to preclinical testing and genome-scale

metabolic models are now feasible. They have been especially useful for developing

drugs targeting pathogens. ¹²

27.7 Behaviour-Based Testing

The advent of wearable technology, ranging from miniature accelerometers to sensors

for various physiological parameters, has made it feasible to undertake real-time,

real-life monitoring of patients taking experimental drugs. This approach, which

for many patients is far more appropriate than hospital monitoring, also makes use

of the enormous computational power now available and the ubiquity of wireless

communications networks.

References

Abbas K, Abbasi A, Dong S, Niu L, Yu L, Chen B, Cai S-M, Hasan Q (2021) Application of network

link prediction in drug discovery. BMC Bioinform 22:187

Fernández A (2010) Transformative concepts for drug design: target wrapping. Springer

Fernández A (2015) Biomolecular interfaces: interactions, functions and drug design. Springer

Fernández A (2016) Physics at the biomolecular interface: fundamentals for molecular targeted

therapy. Springer

Fernández A (2019) Therapeutic disruption of protein complexes with unknown structure: a case

for deep learning. Trends Pharmacol Sci 40:551–554

Fernández A (2020) Artificial intelligence steering molecular therapy in the absence of information

on target structure and regulation. J Chem Inf Model 60:460–466

Fernández A (2021) Artificial intelligence deconstructs drug targeting in vivo by leveraging a

transformer platform. ACS Med Chem Lett 12:1052–1055

Gu C, Kim GB, Kim WJ, Kim HU, Lee SY (2019) Current status and applications of genome-scale

metabolic models. Genome Biol 20:121

Horvath R, Cottier K, Pedersen HC, Ramsden JJ (2008) Multidepth screening of living cells using

optical waveguides. Biosens Bioelectron 24:805–810

von Maltzahn G et al (2012) Nanoparticles that communicate in vivo to amplify tumour targeting.

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12 Gu et al. (2019).