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Statistical Approaches in the Development of Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Oleksandr Sverdlov, Yevgen Ryeznik, Sergei Leonov, Valerii Fedorov
In the biopharmaceutical industry, modeling and simulations have been broadly applied to enhance the efficiency of trials and projects and inform important development decisions (Kowalski, 2019). Recognizing the importance and potential utility of in silico clinical trials, the FDA fully supports their use in biomedical research. In silico clinical trials have been defined as follows:39…In Silico clinical trials use computer models and simulations to develop and assess devices and drugs, including their potential risk to the public, before being tested in live clinical trials. Advanced computer modeling may also prove useful in helping to predict how a drug or device will behave when deployed in the general population or when used in particular circumstances, thereby helping to protect the public from the unintended consequences of side effects and drug interactions. In Silico trials may potentially protect public health, advance personalized treatment, and be executed quickly and for a fraction of the cost of a full scale live trial. The FDA has advocated the use of such systems as an additional innovative research tool. Therefore, the Committee urges FDA to engage with device and drug sponsors to explore greater use, where appropriate, of In Silico trials for advancing new devices and drug therapy applications.
Approaches for Identification and Validation of Antimicrobial Compounds of Plant Origin: A Long Way from the Field to the Market
Published in Mahendra Rai, Chistiane M. Feitosa, Eco-Friendly Biobased Products Used in Microbial Diseases, 2022
Lívia Maria Batista Vilela, Carlos André dos Santos-Silva, Ricardo Salas Roldan-Filho, Pollyanna Michelle da Silva, Marx de Oliveira Lima, José Rafael da Silva Araújo, Wilson Dias de Oliveira, Suyane de Deus e Melo, Madson Allan de Luna Aragão, Thiago Henrique Napoleão, Patrícia Maria Guedes Paiva, Ana Christina Brasileiro-Vidal, Ana Maria Benko-Iseppon
The urgent demand to obtain alternatives against antibiotic-resistant pathogens has driven antimicrobial compounds into the spotlight (Eckert 2011). Since the design and development of a new drug candidate is an expensive process, the use of in silico approaches is frequently applied to improve the discovery of novel compounds and to optimize these molecules in clinical use (Anand et al. 2019; Charoenkwan et al. 2021).
The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Finally, skeptics point out that some of the more enthusiastic claims in relation to the use of AI in drug discovery echo the excitement over computer-aided drug design which began in the early 1980s but has failed to deliver significant examples of new drugs in the clinic. Although in silico modeling techniques are now recognized as important tools in modern drug discovery and development, they have not halted a decline in productivity of the pharmaceutical industry dating back to the mid-1990s. It is possible that some of the more extravagant predictions being made about the ability of AI to revolutionize drug discovery might not be realistic. Critics point out that no AI-discovered drugs have yet reached the approval stage, and that commercial and peer-pressure to introduce these technologies are at play, as was the case with molecular modeling approaches to drug discovery in the 1990s. However, enthusiasts maintain that AI approaches have the potential to pinpoint previously unknown causes of diseases and to accelerate the trend toward treatments designed for patients with specific biological profiles in a Precision Medicine approach (see Chapter 11).
Assessing developability early in the discovery process for novel biologics
Published in mAbs, 2023
Monica L. Fernández-Quintero, Anne Ljungars, Franz Waibl, Victor Greiff, Jan Terje Andersen, Torleif T. Gjølberg, Timothy P. Jenkins, Bjørn Gunnar Voldborg, Lise Marie Grav, Sandeep Kumar, Guy Georges, Hubert Kettenberger, Klaus R. Liedl, Peter M. Tessier, John McCafferty, Andreas H. Laustsen
Recently, a shift has occurred within the discovery and development of biologics, from mainly focusing on high affinity and specificity to the target, hitting the right epitope, and conveying the desired function, to now also taking developability aspects into consideration. This broadened discovery and multidimensional engineering mindset is likely to yield better drug candidates, as well as reducing the number of late-stage failures during drug development. However, in many cases, especially with completely novel types of biologics, it is not always clear what constitutes a good developability profile, although some examples of such profiles (e.g., marketed drug-likeness79 and the Therapeutic Antibody Profiler80) are beginning to emerge. With more sequence information and biophysical data becoming publicly available, the task of establishing guiding principles on developability is becoming more approachable. Within this field, we expect that in silico predictions will play an increasingly larger role early in the discovery process, as they allow for very high-throughput analysis at low cost (when established). However, using in silico models may come with some inherent uncertainties due to biases in existing datasets, and re-evaluating algorithms and both expanding existing and building new datasets will continue to be important. During generation of these datasets, it is likely beneficial to include and explore molecules that may not be predicted to have superior developability profiles.
The use of in silico extreme pathway (ExPa) analysis to identify conserved reproductive transcriptional-regulatory networks in humans, mice, and zebrafish
Published in Systems Biology in Reproductive Medicine, 2023
Overall, ExPa analyses indicated a considerable conservation of reproductive TRNs between mammals and zebrafish. Factor analysis across all aggregated life-stages also supported this observation (Figure 5). Given the reliance of zebrafish sex differentiation on germ cell numbers established during the bipotential stage (Kossack and Draper 2019), the conserved reproductive TRNs between zebrafish and mammals were mostly for the maintenance of sexual differentiation (once it is established). As a result, regardless of a lack of sex determination genes in zebrafish the TRNs responsible for canalizing male vs. female sexual differentiation were conserved with mammalian taxa. The discrete dynamics of gene activations over ontogeny precludes the predominance of hierarchical ‘master’ regulators. In so much is evident whereby knock-outs or mutations of either sex determination or differentiation genes result in sex reversal or reproductive functional dysgenesis (Ono and Harley 2013; Ohnesorg et al. 2014). Therefore evolutionarily, control appears distributed with ‘parliamentary’ decision-making resting with ensembles of genes active at particular developmental stages (albeit their activations being dependent upon those of preceding genes) (Capel 2017). Finally, limitations of the in silico and in vivo model systems are considered below.
Challenges faced in developing an ideal chronic wound model
Published in Expert Opinion on Drug Discovery, 2023
Mandy Li Ling Tan, Jiah Shin Chin, Leigh Madden, David L. Becker
Preclinical animal testing is a requirement for any novel therapeutic prior to entering human clinical trials. However, rising costs and high failure rates have led to the re-evaluation of the value of animal studies. Advancement in technologies have led to many companies incorporating human in silico trials to refine and reduce the use of animals[123]. Briefly, in silico clinical trials utilize computer simulations and modeling of human systems to predict various outcomes such as toxicity and efficacy[123]. In recent years, these in silico trials have been accepted as evidence by different regulatory agencies such as the FDA and European Medicines Agency upon assessment of the credibility of the predictive model used[124]. Nonetheless, a one-to-one replacement of animal testing with in silico trials is generally not practicable due to the complexity of the human physiology. Instead, a combination of both animal and in silico predictive models could contribute to a better mechanistic understanding of the safety and efficacy of a new drug with the human biological system.