Why Us

Before I start, I have to be able to envisage how I will be able to show in man that the drug has the same basic actions as I would have found in the laboratory.

Sir James Whyte Black (1924-2010)

Over the past three decades, more than 1026 compounds have been tested in animal models of acute ischemic stroke (AIS) and at least 114 drug candidates were approved for clinical trials, but none of the above-mentioned compounds that entered clinical trials have been successful [1-3,107].

The translation of positive results of neuroprotective therapy from bench to bedside has been challenging as all neuroprotectants deemed to be effective in rodent models of stroke have been ineffective in humans [4]. These challenges gave rise to concerns that neuroprotection may not be a feasible or practicable strategy in humans [4]. After so many years of failure, it is still controversial whether researchers should continue to pursue neuroprotective strategies for stroke [3].

The reasons for lack of translation and failure remain unclear and resulted in the formation of Stroke Therapy Academic Industry Roundtable (STAIR) by a group of basic and clinical researchers along with representatives from the pharmaceutical industry [2]. STAIR committee relates the possible reasons for the translation failure to the inherent properties of the drug candidates themselves or the specific animal models used to assess them [5].

It seems to us that the failure of attempts of stroke-drug hunting can be ascribed to technical and non-technical issues. To a large degree, the two issues intertwine with each other and constitute a vicious cycle. The future trends of the two issues are inexorable and may cause hard to remedy consequences.

Technical issues including drug R&D strategies, drug discovery approaches, and research quality:

Technically, the rationale for why our idea is likely to work when previous efforts by others have failed can be explained by the following table:

Issues in Drug R&DEfforts by forerunnersOur ideas
ApproachesTarget-based drug discoveryChemocentric phenotypic drug discovery
StrategiesSingle-target neuroprotectionBrain protection with magic shotguns
Research qualityPoor quality of stroke researchImproved quality of stroke research

From our perspective, the vast majority of previous stroke-drug hunters (forerunners) made a series of mistakes, mostly in the issues of preclinical studies: including inappropriate drug discovery approaches, wrong drug R&D strategies,  and poor quality of stroke research. We believe that a combination of all the above mistakes made by previous stroke-drug hunters may have ultimately led to translational failures.

It is well documented that the single-target neuroprotective strategies for AIS won’t work [6-8]. To date, almost all neuroprotective strategies have attempted a monotherapy against a single target [6,8]. Thus, neuroprotectants that target individual factors have limited success in AIS treatment [8]. It is believed that the concept of neuroprotection is perhaps misguided [86]. Neuroprotection is simply not enough for clinical success, and rather the focus needs to shift toward full cerebroprotection (brain protection), in which the entire neurovascular network and neurological structure are protected, rather than solely neuroprotection [86].

Target-based drug discovery (TDD) approaches are predominantly technology and hypothesis-driven and rooted in targeting few components of the complex biological system rather than the whole system [9-11]. The weakness of TDD approach is that the solution to the specific molecular hypotheses may not be relevant to the disease pathogenesis or provide a sufficient therapeutic index [12]. Stroke is a variable, complex and multi-factorial polygenic disease in which multiple convoluted signaling pathways overlap and converge in the pathophysiology of stroke [13-15], which proves to be a heterogeneous group of vascular and neurological disorders [16,17,84,85]. Targeting molecular targets involved in complex disease phenotypes is challenging because the formers are physically or functionally integrated into complex networks, necessary for the fine-tuning of a multitude of physiological functions [18]. Validating targets by TDD for such diseases as AIS has proved to be a formidable task and it is likely to be futile [19]. It was reported that treatment with blockers of the historical classes of targets selected for therapeutic intervention for AIS by TDD approach have never been validated against the role these molecular targets play in AIS victims at a realistic time frame of their availability for treatment [19]. Without solid biological validation, TDD has proven very disappointing [20]. In this regard, the chances of success for hunting new drug for AIS by reductionist approach such as TDD are most likely to continue to carry a high risk of failure [19,106].

Animal models of human disease are cornerstones in the science of drug discovery [21]. It is immediately obvious that the clear discrepancies exist between preclinical animal models used by previous stroke-drug hunters and the clinical population most commonly afflicted by AIS [22]. Thus, the predictive value of animal models of ischemic stroke previously and currently in use is severely compromised and flawed animal models may have contributed to the failure of the translation. In addition, it is well documented that the vast majority of preclinical studies carried out by previous stroke-drug hunters have suffered from quality issues, potentially leading to a distortion of results and dramatically increased the probability of false positives and/or inflated effect sizes [1,23-38].

It is time to learn from the past and stop making the same mistakes over and over again. A dramatic change in methodologies, as well as research culture, is required to develop drugs for the treatment of AIS. It has been found that for neurological and central nervous system (CNS) disorders, magic shotguns (selectively non-selective drugs) are usually better than magic bullets (drugs selective for single molecular targets)[39,40,105]. Targeting all components of the neurovascular network, rather than just the neuron, should be a priority in stroke research, and agents that block multiple events of the injury cascade are more likely to provide brain protection [87]. The concept of brain protection has been widely used in place of neuroprotection [88]. After repeated attempts of failure, numerous scientists finally realize that pleiotropic multimodal brain protection (brain protection with magic shotguns) strategies are supposed to keep the stroke drug discovery from the abyss and are most probably the only hope for pharmacological treatment of AIS [1,6,41-45]. That’s the strategy we are going to have to pursue.

Drug hunting historically has followed two general pathways — phenotypic drug discovery (PDD) approach and TDD approach [46,101]. PDD is biology first, an empirical and observation-based approach that tests compounds directly in a physiologically relevant model system in the absence of a specific molecular target hypothesis [47,101]. Before the introduction of TDD approach, drug discovery was driven primarily by PDD approach, often with limited knowledge of the molecular mechanisms of disease [12,101]. The historical exemplars of approved drugs discovered by PDD highlight that clinically useful and safe medicines can be developed without precise knowledge of the target mechanism [48]. Furthermore, PDD approaches have resulted in the development of many clinically valuable drugs, which would not be developed by TDD programs [48].

Both approaches have their strengths and limitations and the optimal approach for a particular project will depend upon the disease in question [66,98]. The strength of the phenotypic approach is that the assays do not require a prior understanding of the molecular mechanism of drug action, and activity in such assays might be translated into therapeutic impact in a given disease state more effectively than in target-based assays, which are often more artificial [12]. PDD approaches agnostically and simultaneously interrogate multiple molecular targets and signaling pathways of direct biological relevance in a manner that is independent of the veracity of prior target validation studies [49]. Thus phenotypic approaches may be appropriate in cases where the disease mechanism remains a black box such as AIS and must have certain advantages [50].

Historically, lead compound optimization by PDD approach was pursued successfully without target-based optimization — even in the days when animal physiology was the only ‘readout’ [20]. Even though PDD approaches are faced with the challenge of optimizing the molecular properties of candidate drugs without the design parameters provided by prior knowledge of the molecular mechanism of action (MMOA) and the assay throughput is usually low, it is observed that the discovery rate of new molecular entities (NMEs) by PDD was greater than TDD and PDD was the most successful approach for the discovery of first-in-class NMEs [12,51]. Furthermore, evaluation of the discovery strategy by disease area showed that the PDD approach was the most successful for CNS disorders [12]. The phenotypic approach starts from a disease-relevant phenotypic trait in the whole animal as a discovery platform and subsequently identifies the targets controlling this trait [18]. In this approach, the targets identified are causally related to disease, and in addition, they have a high level of inherent validation [18]. PDD approach may revitalize drug discovery for AIS and improve the success rate of drug approval through the discovery of viable lead compounds and identification of novel drug targets [52]. For a disease whose mechanism remains unknown, complex, and multifactorial, choosing PDD as a stroke drug discovery approach may be the most appropriate choice.

PDD has a successful track record of delivering first-in-class drugs [53]. It is a powerful approach to exploit the novel biological space of undrugged or unknown targets and poorly understood disease mechanisms, providing a route to enhance innovation in the pharmaceutical industry and to deliver truly novel therapeutics for unmet medical needs [53]. However, PDD is also a challenging and difficult drug discovery approach on multiple levels [53]. Inventing and developing a new medicine is a long, complex, costly and highly risky process that has few peers in the commercial world [54]. R&D for most of the medicines available today has required 12–24 years for a single new medicine, from starting a project to the launch of a drug product [54]. We all agree that effectiveness in drug discovery is far more important than efficiency [55]; however, as a new startup, we cannot afford to spend so many years and so much money testing a large number of randomly selected-compounds in a systems-based assay in order to generate a chemical starting point (lead compounds). All new drug research programs have to begin with a chemical starting point and the hard part was to choose the chemical starting point for our chemical program [56]. Not surprisingly, both Dr. Paul Janssen and Sir James Black preferred to start a drug discovery program with a biologically active molecule with known pharmacology served as the starting point and many genuinely useful drugs were discovered by this classical chemocentric approach [57,58]. Therefore, we need to hybrid the PDD with chemocentric approaches.

The quality issue which may be ascribed to the failure of neuroprotective agents includes: animal models, animal species for preclinical studies, and experimental design (especially the time window for intervention, sample size, animal inclusion and exclusion criteria, randomization, allocation concealment, blinding, evaluation methods of efficacy, inclusion of r-tPA as a positive control, use of anesthesia, and statistical method) [24,26,33,34,59]. Based on STAIR criteria [5,26], RIGOR guidelines [24], and opinions of experts in this field [23,25,89], all the aforementioned technical issues will be carefully evaluated and fully optimized in our preclinical studies. Not all investigators agree that it is necessary to fulfill the STAIR and RIGOR criteria before advancing to clinical trials [29]. It is unknown which of these criteria are critical for the success of a stroke therapeutic [29]. Even though strict adherence to these criteria for preclinical testing does not guarantee the emergence of highly efficacious drug candidates, it is hoped that carefully following these guidelines may enhance the chances for success [29,60].

All in all, magic shotguns (selectively non-selective drugs) may be better than magic bullets (drugs selective for single molecular targets) for stroke treatment [104-106]. As PDD and chemocentric approaches have made many great discoveries in history that did so much to extend and enhance human life [58,61], it would be unwise to abandon these approaches like the pharma industry did three decades ago [62-64]. Based on realistic considerations, choosing PDD and chemocentric approaches as our stroke drug discovery approach may be the best choice. The importance of improving the quality of preclinical research cannot be overstated and all the technical issues will be carefully evaluated and fully optimized in our preclinical studies.

The nontechnical issues including corporate culture and values:

There is a tendency to believe that the high failure rate is merely technical and that the problem is soluble but we just have not got the right technical plan [65]. However, according to some experts in this field, the problem is more a philosophical one [65]. The switch in the mid-1980s/early 1990s from a phenotypic approach to a target-based approach to drug discovery was followed by changes in corporate culture and values [54,62,63,66-73]. The sweeping changes in corporate culture and values have been ascribed to the problem. The leaders of major pharma companies have incorrectly assumed that R&D was scalable, could be industrialized, and could be driven by detailed metrics (scorecards) and automation [73]. The grand result is a loss of personal accountability, transparency, and the passion of scientists in discovery and development [74]. The supreme loyalty of today’s pharma companies is not primarily directed at patients and their physicians but at shareholders [67,102]. It is quite evident that attitudes driving Big Pharma are unsupportive of science and innovation [67]. The focus on projected sales rather than on the scientific novelty and the medical value of the drugs and, in particular, the obsession with blockbusters, has compromised the creative potential and the innovative power of most Big Parma companies [67]. R&D is degraded to a tool for generating medicines that qualify as blockbusters [67]. Many features of modern corporate culture in pharma environments thwart high-performing drug discovery units [68]. It appears that traditional pharma companies are less likely to contribute innovative ideas to drug discovery than they were in the past [67]. Despite unprecedented investment in pharmaceutical R&D, the number of new drugs approved by drug regulatory authorities remains low [58,74]. Furthermore, the real innovation crisis is that pharmaceutical R&D turns out large numbers of new drugs with few clinical advantages over existing ones [58,75]. With the timescales for drug discovery lengthening and the costs of innovation rising, Big Pharma companies come together by mergers and acquisitions (M&A) to strengthen their product portfolios and their pipelines [67,76]. However, the rationale for major M&A has been challenged, as none has had a detectable effect on new-drug output [74]. It is believed that the hidden business model for pharmaceutical research, sales, and profits has long depended less on the breakthrough research that executives emphasize than on rational actors exploiting ever broader and longer patents and other government protection against normal free-market competition [75]. Outsourcing is now being considered as the best way to increase performance in pharmaceutical R&D by leveraging core competencies, but not yet capable of solving the real problems plaguing drug discovery and development or strategic directions of the pharma industry [77,78].

Paul Ehrlich, the father of medicinal chemistry and the teacher of the world (Magister Mundi) in the medical sciences, attributed research success to the four Gs: Geduld (patience), Geschick (skill), Glück (good fortune) and Geld (money) [79]. Paul Ehrlich’s disciples or followers like Gerhard Domagk, Daniel Bovet, George Hitchings, Gertrude Elion, Paul Janssen, James Black, and others followed the iterative evolutionary process invented by Ehrlich and their endeavors contributed to the golden age of drug discovery (ca. 1935-1975) [56,58,61,80,104]. The triumphant pervasiveness during the golden age of drug discovery could be attributed to ingredients such as disease-focused drug discovery paradigm, concept-driven projects, passionate commitment, intense concentration, courage, determination, dedication, perseverance, tenacity, hard work, self-motivation, imagination, intuition, insight, good judgment, never-ending curiosity, culture of innovation, individual creativity and innovation, objectivity, great individuality, sense of freedom, open mind, expertise, experience, and serendipity [11,56,66,68,80,91-100,103]. Sadly, most, if not all of the elements of success mentioned above have been ignored or abandoned by today’s pharma companies [54,68,80,81]. Both pharma and academia should bear in mind that all of us may most likely rely on these elements of success again to develop new therapies against life-threatening or fatal diseases [90]. In Arzna Labs, we respect and cherish the above pioneers who had a robust faith in the spiritual value of natural science, and their success factors will be adopted and implemented by us. We really hope to carry forward the faith, courage, and dedication of those pioneers who have gone before us in the history of drug discovery.

In real-world practice, stroke remains an incurable disease in most cases, although reperfusion therapy has made significant progress in the treatment of AIS [82,83]. We have a long way to go. Arzna Labs is engaged in the development of novel therapies directed at improving the treatment of stroke. Thanks to new ideas, innovative approaches, and improved animal modeling of factors involved in human stroke, we are optimistic that new therapies for the treatment of stroke will be developed. Arzna Labs would not desist until we achieved our desired goal.

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