Author: Erin Trachet | Associate Director, Scientific Development
Date: December 2021
New oncology drugs only have a 3.4% success rate once making it from Phase I clinical trials to FDA approval. This is the lowest success rate among the 21 major disease indications.1 This poor success rate leaves everyone, from the patients to the benchtop scientists, questioning the validity of the models and the resulting data. これは、癌研究の世界では今に始まったことではありません。私たちは皆、宿主をだまし、生き残りをかけて適応していく病気を治療しようとしているのです。
Probability (%) of success(2) by clinical trial phase and therapeutic area
Chi Heem Wong, Kien Wei Siah, Andrew W Lo. Estimation of clinical trial success rates and related parameters. Biostatistics 2019;20(2):273-286.
Adapted from the Wong, Siah and Lo publication (table shown above), it is clear that there is a fundamental disconnect between preclinical data and clinical results. Nine out of 10 attempts to bring a new oncology therapy to the clinic will fail. This is far less than other therapeutic areas, such as cardiovascular and inflammation. The very low success rate in oncology leads drug companies and the FDA to be more lenient when it comes to providing new therapies to the desperate patient population. 残念ながらこのことが、臨床現場ですぐに失敗に終わる薬剤へとつながっています。 In a span of ~10 years, there were 9,985 new drugs entering Phase I clinical trials with 31.2% (3,163) for oncology indications alone.1,2 The high failure rate leads to industry criticism that the traditional preclinical animal models are, at best, limited in their power of predictability and, at worst, grossly inaccurate. これが責任転嫁されやすい原因かもしれません。現在の前臨床動物モデルを改善し発展させることが、これらの可能性を知り、同じように重要なこれらが持つ制限を理解するためには不可欠です。従来の前臨床（異種移植および同系）モデルは今まで機能しており、これからも非常に貴重なツールであり続けます。 However, the interpretation of the data that are produced by these models and how they are used to predict clinical response may be where the biggest discrepancies lie.
To mimic more closely the human disease state, many human and murine tumor cell lines have been further validated as orthotopic implants. Implanting the human or murine tumor cells in the tissue of origin can result in a pathological profile that recapitulates human disease and can increase the rate of metastatic involvement when compared to the traditional subcutaneous models.3 As with all models, there are limitations with orthotopic implants; primarily, monitoring disease progression can be limited to survival endpoints which that are not ideal. The optimal situation is to take advantage of the orthotopic implant by using either clinically translatable imaging technology (Magnetic resonance imaging or computerized tomography) or optical imaging (Bioluminescence imaging or fluorescence molecular tomographic imaging). The ability to use imaging technology (clinically translatable or optical) allows for evaluation of a solid tumor or hematological cancer in the same animal over time, as the work is done in the clinic. Tracking disease burden and response in this manner has the potential to be a powerful tool in translating preclinical activity (response) of a new drug into clinical success.
In recent years, there have been significant enhancements in the utilization of patient-derived xenografts (PDXs). In a retrospective analysis of cytotoxic and targeted therapies, PDX models were clearly predictive (~90% accurate) of clinical outcome when dosed at clinically relevant dosage levels.3 This is a significant improvement in the cancer research field, but there are limitations with these models as well. Obtaining fresh human tissue is challenging and chances of successful engraftment, even in a severely immunocompromised mouse, is approximately 30%.3 If engraftment is successful, maintaining these PDX lines as low passage models for future use presents even more complications. The logistics of obtaining and maintaining models, from tissue acquisition to running efficacy studies, along with the overall cost, may be the most significant hindrances to the widespread use and acceptance of these PDX models.
Clinically, the most frequently utilized endpoints to evaluate the effectiveness of a therapy are an industry-standardized set of terms and definitions (Response Evaluation Criteria in Solid Tumors or RECIST criteria). Paramount to this list is the responsiveness of the disease to treatment: complete response (CR), partial response (PR), and overall increase in survival. CR は原発腫瘍塊の完全な退行として定義されます。 PRs are defined as a partial reduction in the primary tumor by ~30%. Therapies are considered successful if they induce either CRs or PRs that can lead to a positive impact on survival. However, in the preclinical setting the commonly used endpoints are tumor-growth inhibition and tumor-growth delay; both are defined as a slowing of disease progression. Unfortunately, tumor-growth inhibition does not directly correlate to an overall increase in survival. これが重大な違いとなります。研究コミュニティ内の前臨床有効性データに対する評価はあまり厳密なものではなく、臨床からの同様のデータに対するものとは一連の基準が異なります。 The lower standards allow for more drugs to go through to clinical trials resulting in a greater number of clinical failures. 前臨床と臨床の基準を一致させるというこの考え方は、新しいものではありません。 In an editorial published in the Journal of the National Cancer Institute, the authors called for a consensus among drug developers that unequivocally defines successful preclinical endpoints.4 If we can collectively raise the bar to more stringent preclinical criteria for the evaluation of novel cancer treatments, we could possibly reduce the failure rate in the clinic. これにより、臨床医や患者が前臨床データをレビューする際の信頼性が高まります。
今となっては、変えることは困難であり、一晩で何かが変わることを期待するのは甘い考えですが、今認識と実践を少し変えることで、後での大きな影響を与えられる可能性があります。 The preclinical models that we have invested years and years in developing are effective, if we use them correctly. このモデルは、簡潔なプロトコルデザインから始まり、一貫したデータ評価で終わります。 In clinical trials, new drugs will be facing patients with established disease. 研究コミュニティはこの仮定を採用して、より厳格な前臨床実験を設計できます。臨床では、治療開始時の生存を確実にするため、血管床が配置された元の組織内に腫瘍がしっかりと定着します。 We can mimic this environment preclinically with either subcutaneous or orthotopic implants by allowing the tumor to grow and become more established. 腫瘍が定着するまでにかかる時間は、腫瘍株とインプラントの位置によって大きく異なります。皮下腫瘍は標準的なカリパスで簡単に監視でき、成長を確実に進行させることができます。 It is trickier with orthotopic models to ensure that the tumor is actively growing unless you have the ability to image the tumors over time. This relatively straightforward step would save the time and money that is wasted on false positive results generated from studies designed to treat tumors that are not at all or only barely established. At the time of final data analyses, scientists and drug developers need to alter how they define an active new drug. Using the clinical standards as a guideline for activity would decrease the number of new drugs that inevitably fail being pushed into the clinic. This starts with a shift away from tumor-growth inhibition endpoints to the clinically translatable endpoints CRs, PRs, and overall increase in survival.
This is no small request. Trying to convince an entire field of scientists and drug developers to look at their preclinical efficacy data more diligently and to hold it to a more robust set of standards will clearly impact the perceived success rate. However, if more effort is invested into optimizing these new drugs preclinically rather than pushing them into the clinic too early, their chances of real success would increase. All of this, in the long run, would save time, money, and lives potentially.
For more information regarding Labcorp’s Preclinical tumor models and imaging capabilities, please visits: https://drugdevelopment.labcorp.com/industry-solutions/oncology/preclinical/models.html or
1 Chi Heem Wong, Kien Wei Siah, Andrew W Lo. Estimation of clinical trial success rates and related parameters. Biostatistics 2019;20(2):273-286.
2 Kamb A. What's wrong with our cancer models? Nat Rev Drug Discov. 2005; 4(2):161-165.
3 Ruggeri BA, Camp F, Miknyoczki S. Animal models of disease: Pre-clinical animal models of cancer and their applications and utility in drug discovery. Biochem Pharmacol. 2014; Vol. 87(1):150-161.
4 Bertotti A, Trusolino L. From bench to bedside: does preclinical practice in translational oncology need some rebuilding? J Natl Cancer Inst. 2013;105(19):1426-1427.