Three research strategies, old cups, new wines, old medicines, new vitality

Author: Drug Hunter

"Although a bit of an exaggeration, there is a lot of truthin the saying that we do not need to find new drugs; rather we need to find the patients who can benefit from existing drugs"-Christopher Lipinski

The discovery of a new drug will inevitably go through the transformation process of non-drugs ~ class drugs ~ finished drugs, which requires high investment and unknown risks.

In view of the high failure rate of clinical studies of new drugs in recent years, finding new indications for existing drugs has become a very attractive and new strategy for optimizing the cost/benefit of pharmaceutical companies

In recent years, the new use of old drugs as a drug development strategy has received more and more attention, and a large number of new indication drugs have been born (Table 1).

Strategy 1: Develop based on the side effects of existing drugs

Accidental discovery based on the side effects of existing drugs is the most important strategy for the new use of early old drugs, and a series of new indication drugs have been developed from this, such as dapoxetine, chlorpromazine, elonnised, finasteride amine, minoxidil, sildenafil, topiramate, and the like

1.

The dapoxetine developed by Eli Lilly was originally used as an analgesic and antidepressant drug.

2.

Initially, the phosphodiesterase-5 (PDE5) inhibitor sildenafil developed by Pfizer was mainly used in clinical research to relieve angina pectoris

Strategy 2: Develop based on the existing mechanism

1.

In the 1980s, the duloxetine developed by Eli Lilly was originally used as an antidepressant.

2.

In the 1950s, thalidomide had a bad reputation for its initial indication-the treatment of morning sickness leading to seal limbs.

Strategy 3: Based on genome, pharmacological network and signal pathway analysis

Sunitinib and Dasatinib for breast cancer brain metastasis

Cancer has the characteristics of heterogeneity and complexity.

The calculation module includes differential analysis, enrichment analysis, cancer signal bridge (CSB) analysis, network mechanism analysis, survival analysis and relocation analysis.

Cancer signal bridge (CSB) analysis connects signal pathways with cancer-related proteins.

For example, a specific CSB contains four proteins BRCA1, HSPA8, GRB2 and NPM1, which can form four PPIs: BRCA1↔HSPA8, BRCA1↔NPM1, GRB2↔HSPA8 and GRB2↔NPM1

By using CSB, NPM1 can be linked to the EGF pathway based on the GRB2 protein, or linked to the E2F transcription factor pathway based on the BRCA1 protein
.
Network mechanism analysis is mainly based on difference analysis and enrichment analysis results and CSB signal network
.

Based on network modeling, the obtained pre-network signals involve the largest number of signal pathways and include the genes with the largest differences in gene signatures
.
The survival analysis module is then used to correlate the hypothetical signal pathway with the available clinical information of the patient (such as metastasis-free survival time)
.

Hierarchical clustering of pathways and Kaplan-Meier survival analysis are performed iteratively to filter out high-confidence protein pathways that are critical to patient survival and metastasis.
Drugs with high confidence that target proteins and genes have very high confidence.
High potential to interfere with specific gene profiles in cancer cells
.

The researchers identified 15 repositioned drug candidates through the use of computing modules
.
Among them, 10 drugs meet the "five rules of drug-like drugs" for central nervous system drugs, indicating that they may penetrate the blood-brain barrier
.
Among them, sunitinib (approved for the treatment of advanced renal cell carcinoma and gastrointestinal stromal tumors) and dasatinib (approved for the treatment of chronic myelogenous leukemia), there has been no previous report on its association with breast cancer in the brain The transfer is connected through their targets in the signal transduction network (RET and KDR, sunitinib; FYN, dasatinib), using experimental modules to verify
.
The results showed that both of these drugs showed the potential to significantly reduce brain metastasis
.

Figure 1 An overview of the technical pipeline for cancer drug repositioning.
(From Zhao et al.
Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases.
CancerRes.
, 2013, 73, 6149-6163).

Antidepressant Amoxapine Used to Relieve the Toxicity of Irinotecan

Irinotecan is a common type I DNA topoisomerase inhibitor, used for malignant tumors, such as brain cancer, colon cancer and lung cancer, and refractory lymphoma
.
Delayed diarrhea is the main adverse reaction, the incidence is as high as 88%, and about 20%-30% of patients must terminate treatment due to severe diarrhea (CTCAE grade 3-4)
.
This side effect severely reduces the patient's quality of life, and limits the scope of clinical application of irinotecan and intensive treatment options
.
Studies have shown that β-glucuronidase (GUS) of intestinal bacteria can convert the non-toxic irinotecan metabolite SN-38G into toxic SN-38, thereby inducing intestinal side effects
.
GUS is a brand-new drug target, and there is no effective lead compound yet, so the researchers screened 1,280 listed drugs in Prestwick Chemical Library® ( of which the antidepressant amoxapine was identified as One of the most promising potential drugs against bacterial GUS
.
Subsequent studies have shown that amoxapine and its main metabolites, 7-hydroxy amoxapine and 8-hydroxy amoxapine, are on average GUS inhibitors and can synergistically inhibit irinotecan-induced diarrhea
.
As an old drug with known drug properties and safety, the antidepressant amoxapine can be used in a combination of irinotecan chemotherapy in a relatively short period of time
.

Summarize

Old drugs have proven pharmacokinetic properties and safety.
Therefore, new strategies for old drugs will avoid the high clinical failure rate caused by ADMET, thereby reducing R&D costs and shortening the time to market
.
In the development strategy of old drugs and new drugs, accidental discovery based on clinical has indeed contributed to many successful cases, but they are often in a relatively passive state of research and development.
The existing traditional methods based on target or phenotype screening are also powerful development strategies for old drugs and new drugs.

.

These traditional methods still have many shortcomings.
The first is the difficulty of drug availability.
Although many suppliers provide a variety of commonly used compounds, it is impossible to cover all candidate compounds that have been approved or studied around the world
.

The National Institutes of Health's translational science inventory has 3,800 approved or researched compound entities, which provide free electronic resources, but the small molecule compounds used for screening can only be obtained through limited cooperation
.
Second, even if the screening is limited to existing listed drugs, the application of a large number of screening platforms still exceeds the operational capacity of any pharmaceutical company
.
Take rare diseases as an example.
According to the FDA's definition of rare diseases, there are approximately 6000 rare diseases in the United States, most of which do not have effective medical programs and screening models
.

Therefore, calculation-based methods are becoming the mainstream of systemic research on new use of old drugs, comprehensively using drug molecule-target interactions, PPIs, target-disease associations, signaling pathways and pharmacological networks, disease-related genomics, clinical monitoring and big data Public data such as health records will be able to further reduce the investment in the research and development process of new use of old drugs and increase the hit rate of new indications
.

references

1 Ashburn, TT and Tor, KB (2004) Drugrepositioning: identifying and developing new uses for existing drugs.
Nat.
Rev.
Drug Discovery, 3, 673–683.

2 Novac, N.
(2013) Challenges and opportunities of drug repositioning.
Trends Pharmacol.
Sci.
, 34, 267–272.

3 Zhao, H.
, Jin, G.
, Cui, K.
, Ren, D.
,Liu, T.
, Chen, P.
, Wong, S.
, Li, F.
, Fan, Y.
, Rodriguez, A.
, Chang, J.
, and Wong, STC (2013) Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases.
CancerRes.
, 73, 6149–6163.

4 Norbert H.
, Helmut B.
Drug Selectivity: An Evolving Concept in Medicinal Chemistry, First Edition, 2018.