This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

The paramount prerequisite for effective anti-cancer drugs is their ability to eradicate malignant cells while sparing non-cancer cells. The divergence in properties between malignant and non-cancer cells establishes a "therapeutic window," a critical consideration for achieving desirable treatment outcomes. Central to this is the imperative of a cancer drug's "selectivity and specificity." Taxanes, a pivotal class of successful anti-cancer drugs, continue to serve as the linchpin of cancer treatment due to their efficacy across a spectrum of cancer types. Operating as broad-spectrum chemotherapeutic agents, taxanes exert cytotoxic effects on proliferative cancer cells by binding to and stabilizing microtubules, disrupting mitosis, inducing mitotic catastrophe, and resulting in cell death. The distinct proliferative nature of cancer cells, as opposed to less proliferative non-cancer cells, affords taxanes a measure of specificity and selectivity. Nevertheless, sporadic yet recurring evidence suggests that taxanes also operate through non-mitotic mechanisms. Taxanes' binding and stabilization of microtubules lead to micronucleation and subsequent cell death, impacting both mitotic and non-mitotic cells. Recent discoveries indicate that the flexible and weakened nuclear envelope of malignant cells renders them sensitive to taxane-mediated micronucleation and cell death during various phases of the cell cycle. Conversely, non-cancerous cells typically exhibit a more robust and sturdy nuclear envelope, rendering them more tolerant to taxane-induced nuclear envelope fragmentation and subsequent micronucleation and cell death. The expression levels of nuclear envelope structural proteins, particularly Lamin A/C, emerge as indicators of taxane sensitivity. This evolving understanding underscores that nuclear envelope malleability, in conjunction with a high proliferation rate, is a pivotal determinant of taxane specificity and selectivity against malignant cells. These insights necessitate reconsidering oncological strategies to augment taxane efficacy, overcome resistance, and mitigate side effects.

Approximately 28 million individuals in the United States face the risk of developing precancerous colonic adenomas (polyps) and potentially progressing to colorectal cancer (CRC). While a promising strategy for CRC prevention involves pharmacological intervention, such as cancer chemoprevention or interception, currently, there are no FDA-approved drugs capable of preventing the formation or progression of adenomas to adenocarcinoma. Numerous clinical, epidemiological, and preclinical studies have offered compelling evidence supporting the efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs) in CRC chemoprevention. However, the prolonged use of NSAIDs is not FDA-approved due to potential life-threatening toxicities resulting from cyclooxygenase (COX) inhibition and the depletion of physiological prostaglandins. Despite indications that the COX inhibitory activity of NSAIDs may not be essential for their antineoplastic effects, the absence of a well-defined target impeded the development of derivatives that do not inhibit COX. Earlier research suggests that the inhibition of cyclic guanosine monophosphate phosphodiesterase (cGMP PDE) may be responsible, at least in part, for the antineoplastic activity of the NSAID sulindac. This could potentially offer a novel target for CRC chemoprevention. To identify the cGMP PDE isozyme(s) contributing to the antineoplastic activity of sulindac, we synthesized a chemically diverse library of over 1500 compounds, all sharing the indene scaffold of sulindac. Subsequently, we screened these compounds for their impact on cancer cell growth and PDE inhibitory activity. From this screening, a series of lead compounds emerged. These compounds lacked COX-1 and COX-2 inhibitory activity, surpassing sulindac in potency to inhibit CRC cell growth. Importantly, they demonstrated greater selectivity by not affecting normal cell growth. Through chemical optimization, we identified several development candidates that selectively inhibit PDE5 and/or PDE10. These compounds activate cGMP/PKG signaling, suppressing Wnt/β-catenin transcription. This action counters the growth advantages resulting from APC or CTNNB1 mutations, which are responsible for most human CRCs. This review delves into the scientific literature supporting PDE5 and/or PDE10 as potential targets for CRC chemoprevention or interception. Our findings suggest a promising avenue for developing drugs that may effectively intervene in the progression of colorectal cancer, offering hope for improved preventive strategies in the future.